Test.ipynb

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    "# Final Capstone Project\n",
    "## Best location for an Italian restaurant among top US cities - test edit\n",
    "\n",
    "#### by Ignacio de Juan\n",
    "#### November 2020, Final Project Assignment for IBM Data Science Course\n",
    "\n",
    "### 1. Introduction / Business Problem\n",
    "\n",
    "We need to take the decision of where is the best location to open a new restaurant of a certain cuisine type in a top city of the USA. In this case it will be Italian cuisine.\n",
    "\n",
    "The decision of opening a new restaurant should take into consideration the competition, demand and likelihood of success taken into account as much information available.\n",
    "\n",
    "Through this exercise we will address the following questions:\n",
    "\n",
    "- Which are the cities with a highest potential based on the level of concentration of these type of restaurants?\n",
    "- Is the size of the local Italian descent community a variable that affects the number of Italian restaurants? \n",
    "- What other factors should we take into account for such a decision? size of the city, other cuisine competition?\n",
    "\n",
    "With the aid of Wikipedia tables and the Foursquare API we will navigate through the data, looking for insights and arguments to help our final decision.  \n",
    "\n",
    "Lastly, we will point out the caveats and potential further developments of this exercise.\n",
    "\n",
    "*Target Audience*\n",
    "\n",
    "This analysis can be of interest to entrepreneurs, investors and corporates that are considering the set-up of a new business or the expansion of their current one. The provided framework can be useful to determine where to open the restaurant and what strategy should be followed."
   ]
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   "source": [
    "### 2. Data\n",
    "\n",
    "We will take data from top US cities, ranked by population. The reason for taking the largest cities is to achieve a sufficiently large dataset to draw valid conclusions. We will be using Wikipedia to identify the target cities of our study.\n",
    "\n",
    "Foursquare will serve as our database for the restaurant data. \n",
    "\n",
    "In the following sections we describe in practical terms, with examples, how is this information obtained and put in the right format to be used in the subsequent analysis phase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Methodology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dotenv extension is already loaded. To reload it, use:\n",
      "  %reload_ext dotenv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import json_normalize\n",
    "import numpy as np\n",
    "import requests\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "mpl.style.use(['seaborn'])\n",
    "\n",
    "# !pip install python-dotenv\n",
    "\n",
    "# Credentials file\n",
    "%load_ext dotenv\n",
    "%dotenv\n",
    "\n",
    "import os\n",
    "\n",
    "import json\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "import folium # map rendering library\n",
    "\n",
    "\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieving the Data \n",
    "\n",
    "##### US cities data\n",
    "\n",
    "We retrieve the list of US cities with their population and other relevant data\n",
    "\n",
    "Source: Wikipedia, https://en.m.wikipedia.org/wiki/List_of_United_States_cities_by_population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "     2019rank               City              State[c]  2019estimate  \\\n",
      "0           1   New York City[d]              New York       8336817   \n",
      "1           2        Los Angeles            California       3979576   \n",
      "2           3            Chicago              Illinois       2693976   \n",
      "3           4         Houston[3]                 Texas       2320268   \n",
      "4           5            Phoenix               Arizona       1680992   \n",
      "5           6    Philadelphia[e]          Pennsylvania       1584064   \n",
      "6           7        San Antonio                 Texas       1547253   \n",
      "7           8          San Diego            California       1423851   \n",
      "8           9             Dallas                 Texas       1343573   \n",
      "9          10           San Jose            California       1021795   \n",
      "10         11             Austin                 Texas        978908   \n",
      "11         12    Jacksonville[f]               Florida        911507   \n",
      "12         13         Fort Worth                 Texas        909585   \n",
      "13         14           Columbus                  Ohio        898553   \n",
      "14         15          Charlotte        North Carolina        885708   \n",
      "15         16   San Francisco[g]            California        881549   \n",
      "16         17    Indianapolis[h]               Indiana        876384   \n",
      "17         18            Seattle            Washington        753675   \n",
      "18         19          Denver[i]              Colorado        727211   \n",
      "19         20      Washington[j]  District of Columbia        705749   \n",
      "20         21             Boston         Massachusetts        692600   \n",
      "21         22            El Paso                 Texas        681728   \n",
      "22         23       Nashville[k]             Tennessee        670820   \n",
      "23         24            Detroit              Michigan        670031   \n",
      "24         25      Oklahoma City              Oklahoma        655057   \n",
      "25         26           Portland                Oregon        654741   \n",
      "26         27          Las Vegas                Nevada        651319   \n",
      "27         28            Memphis             Tennessee        651073   \n",
      "28         29      Louisville[l]              Kentucky        617638   \n",
      "29         30       Baltimore[m]              Maryland        593490   \n",
      "30         31          Milwaukee             Wisconsin        590157   \n",
      "31         32        Albuquerque            New Mexico        560513   \n",
      "32         33             Tucson               Arizona        548073   \n",
      "33         34             Fresno            California        531576   \n",
      "34         35               Mesa               Arizona        518012   \n",
      "35         36         Sacramento            California        513624   \n",
      "36         37            Atlanta               Georgia        506811   \n",
      "37         38        Kansas City              Missouri        495327   \n",
      "38         39   Colorado Springs              Colorado        478221   \n",
      "39         40              Omaha              Nebraska        478192   \n",
      "40         41            Raleigh        North Carolina        474069   \n",
      "41         42              Miami               Florida        467963   \n",
      "42         43         Long Beach            California        462628   \n",
      "43         44  Virginia Beach[m]              Virginia        449974   \n",
      "44         45            Oakland            California        433031   \n",
      "45         46        Minneapolis             Minnesota        429606   \n",
      "46         47              Tulsa              Oklahoma        401190   \n",
      "47         48              Tampa               Florida        399700   \n",
      "48         49          Arlington                 Texas        398854   \n",
      "49         50     New Orleans[n]             Louisiana        390144   \n",
      "50         51            Wichita                Kansas        389938   \n",
      "51         52        Bakersfield            California        384145   \n",
      "52         53          Cleveland                  Ohio        381009   \n",
      "53         54             Aurora              Colorado        379289   \n",
      "54         55            Anaheim            California        350365   \n",
      "55         56        Honolulu[b]                Hawaii        345064   \n",
      "56         57          Santa Ana            California        332318   \n",
      "57         58          Riverside            California        331360   \n",
      "58         59     Corpus Christi                 Texas        326586   \n",
      "59         60       Lexington[o]              Kentucky        323152   \n",
      "60         61          Henderson                Nevada        320189   \n",
      "61         62           Stockton            California        312697   \n",
      "62         63         Saint Paul             Minnesota        308096   \n",
      "63         64         Cincinnati                  Ohio        303940   \n",
      "64         65       St. Louis[m]              Missouri        300576   \n",
      "65         66         Pittsburgh          Pennsylvania        300286   \n",
      "66         67         Greensboro        North Carolina        296710   \n",
      "67         68            Lincoln              Nebraska        289102   \n",
      "68         69       Anchorage[p]                Alaska        288000   \n",
      "69         70              Plano                 Texas        287677   \n",
      "70         71            Orlando               Florida        287442   \n",
      "71         72             Irvine            California        287401   \n",
      "72         73             Newark            New Jersey        282011   \n",
      "73         74             Durham        North Carolina        278993   \n",
      "74         75        Chula Vista            California        274492   \n",
      "75         76             Toledo                  Ohio        272779   \n",
      "76         77         Fort Wayne               Indiana        270402   \n",
      "77         78     St. Petersburg               Florida        265351   \n",
      "78         79             Laredo                 Texas        262491   \n",
      "79         80        Jersey City            New Jersey        262075   \n",
      "80         81           Chandler               Arizona        261165   \n",
      "81         82            Madison             Wisconsin        259680   \n",
      "82         83            Lubbock                 Texas        258862   \n",
      "83         84         Scottsdale               Arizona        258069   \n",
      "84         85               Reno                Nevada        255601   \n",
      "85         86            Buffalo              New York        255284   \n",
      "86         87         Gilbert[q]               Arizona        254114   \n",
      "87         88           Glendale               Arizona        252381   \n",
      "88         89    North Las Vegas                Nevada        251974   \n",
      "89         90      Winston–Salem        North Carolina        247945   \n",
      "90         91      Chesapeake[m]              Virginia        244835   \n",
      "91         92         Norfolk[m]              Virginia        242742   \n",
      "92         93            Fremont            California        241110   \n",
      "93         94            Garland                 Texas        239928   \n",
      "94         95             Irving                 Texas        239798   \n",
      "95         96            Hialeah               Florida        233339   \n",
      "96         97        Richmond[m]              Virginia        230436   \n",
      "97         98           Boise[r]                 Idaho        228959   \n",
      "98         99            Spokane            Washington        222081   \n",
      "99        100     Baton Rouge[s]             Louisiana        220236   \n",
      "100       101             Tacoma            Washington        217827   \n",
      "101       102     San Bernardino            California        215784   \n",
      "102       103            Modesto            California        215196   \n",
      "103       104            Fontana            California        214547   \n",
      "104       105         Des Moines                  Iowa        214237   \n",
      "105       106      Moreno Valley            California        213055   \n",
      "106       107      Santa Clarita            California        212979   \n",
      "107       108       Fayetteville        North Carolina        211657   \n",
      "108       109         Birmingham               Alabama        209403   \n",
      "109       110             Oxnard            California        208881   \n",
      "110       111          Rochester              New York        205695   \n",
      "111       112     Port St. Lucie               Florida        201846   \n",
      "112       113       Grand Rapids              Michigan        201013   \n",
      "113       114         Huntsville               Alabama        200574   \n",
      "114       115     Salt Lake City                  Utah        200567   \n",
      "115       116             Frisco                 Texas        200490   \n",
      "116       117            Yonkers              New York        200370   \n",
      "117       118           Amarillo                 Texas        199371   \n",
      "118       119           Glendale            California        199303   \n",
      "119       120   Huntington Beach            California        199223   \n",
      "120       121           McKinney                 Texas        199177   \n",
      "121       122         Montgomery               Alabama        198525   \n",
      "122       123         Augusta[t]               Georgia        197888   \n",
      "123       124             Aurora              Illinois        197757   \n",
      "124       125              Akron                  Ohio        197597   \n",
      "125       126        Little Rock              Arkansas        197312   \n",
      "126       127              Tempe               Arizona        195805   \n",
      "127       128        Columbus[u]               Georgia        195769   \n",
      "128       129      Overland Park                Kansas        195494   \n",
      "129       130      Grand Prairie                 Texas        194543   \n",
      "130       131        Tallahassee               Florida        194500   \n",
      "131       132         Cape Coral               Florida        194495   \n",
      "132       133             Mobile               Alabama        188720   \n",
      "133       134          Knoxville             Tennessee        187603   \n",
      "134       135         Shreveport             Louisiana        187112   \n",
      "135       136          Worcester         Massachusetts        185428   \n",
      "136       137            Ontario            California        185010   \n",
      "137       138          Vancouver            Washington        184463   \n",
      "138       139        Sioux Falls          South Dakota        183793   \n",
      "139       140        Chattanooga             Tennessee        182799   \n",
      "140       141        Brownsville                 Texas        182781   \n",
      "141       142    Fort Lauderdale               Florida        182437   \n",
      "142       143         Providence          Rhode Island        179883   \n",
      "143       144    Newport News[m]              Virginia        179225   \n",
      "144       145   Rancho Cucamonga            California        177603   \n",
      "145       146         Santa Rosa            California        176753   \n",
      "146       147             Peoria               Arizona        175961   \n",
      "147       148          Oceanside            California        175742   \n",
      "148       149          Elk Grove            California        174775   \n",
      "149       150              Salem                Oregon        174365   \n",
      "150       151     Pembroke Pines               Florida        173591   \n",
      "151       152             Eugene                Oregon        172622   \n",
      "152       153       Garden Grove            California        171644   \n",
      "153       154            Cary[v]        North Carolina        170282   \n",
      "154       155       Fort Collins              Colorado        170243   \n",
      "155       156             Corona            California        169868   \n",
      "156       157        Springfield              Missouri        167882   \n",
      "157       158            Jackson           Mississippi        160628   \n",
      "158       159      Alexandria[m]              Virginia        159428   \n",
      "159       160            Hayward            California        159203   \n",
      "160       161        Clarksville             Tennessee        158146   \n",
      "161       162           Lakewood              Colorado        157935   \n",
      "162       163          Lancaster            California        157601   \n",
      "163       164            Salinas            California        155465   \n",
      "164       165           Palmdale            California        155079   \n",
      "165       166          Hollywood               Florida        154817   \n",
      "166       167        Springfield         Massachusetts        153606   \n",
      "167       168           Macon[w]               Georgia        153159   \n",
      "168       169     Kansas City[x]                Kansas        152960   \n",
      "169       170          Sunnyvale            California        152703   \n",
      "170       171             Pomona            California        151691   \n",
      "171       172            Killeen                 Texas        151666   \n",
      "172       173          Escondido            California        151625   \n",
      "173       174           Pasadena                 Texas        151227   \n",
      "174       175         Naperville              Illinois        148449   \n",
      "175       176           Bellevue            Washington        148164   \n",
      "176       177             Joliet              Illinois        147344   \n",
      "177       178       Murfreesboro             Tennessee        146900   \n",
      "178       179            Midland                 Texas        146038   \n",
      "179       180           Rockford              Illinois        145609   \n",
      "180       181           Paterson            New Jersey        145233   \n",
      "181       182           Savannah               Georgia        144464   \n",
      "182       183         Bridgeport           Connecticut        144399   \n",
      "183       184           Torrance            California        143592   \n",
      "184       185            McAllen                 Texas        143268   \n",
      "185       186           Syracuse              New York        142327   \n",
      "186       187           Surprise               Arizona        141664   \n",
      "187       188             Denton                 Texas        141541   \n",
      "188       189          Roseville            California        141500   \n",
      "189       190           Thornton              Colorado        141464   \n",
      "190       191            Miramar               Florida        141191   \n",
      "191       192           Pasadena            California        141029   \n",
      "192       193           Mesquite                 Texas        140937   \n",
      "193       194             Olathe                Kansas        140545   \n",
      "194       195             Dayton                  Ohio        140407   \n",
      "195       196         Carrollton                 Texas        139248   \n",
      "196       197               Waco                 Texas        139236   \n",
      "197       198             Orange            California        138669   \n",
      "198       199          Fullerton            California        138632   \n",
      "199       200         Charleston        South Carolina        137566   \n",
      "200       201   West Valley City                  Utah        135248   \n",
      "201       202            Visalia            California        134605   \n",
      "202       203         Hampton[m]              Virginia        134510   \n",
      "203       204        Gainesville               Florida        133997   \n",
      "204       205             Warren              Michigan        133943   \n",
      "205       206      Coral Springs               Florida        133759   \n",
      "206       207       Cedar Rapids                  Iowa        133562   \n",
      "207       208         Round Rock                 Texas        133372   \n",
      "208       209   Sterling Heights              Michigan        132438   \n",
      "209       210               Kent            Washington        132319   \n",
      "210       211           Columbia        South Carolina        131674   \n",
      "211       212        Santa Clara            California        130365   \n",
      "212       213          New Haven           Connecticut        130250   \n",
      "213       214           Stamford           Connecticut        129638   \n",
      "214       215            Concord            California        129295   \n",
      "215       216          Elizabeth            New Jersey        129216   \n",
      "216       217          Athens[y]               Georgia        126913   \n",
      "217       218      Thousand Oaks            California        126813   \n",
      "218       219       Lafayette[z]             Louisiana        126185   \n",
      "219       220        Simi Valley            California        125613   \n",
      "220       221             Topeka                Kansas        125310   \n",
      "221       222             Norman              Oklahoma        124880   \n",
      "222       223              Fargo          North Dakota        124662   \n",
      "223       224         Wilmington        North Carolina        123744   \n",
      "224       225            Abilene                 Texas        123420   \n",
      "225       226             Odessa                 Texas        123334   \n",
      "226       227           Columbia              Missouri        123195   \n",
      "227       228           Pearland                 Texas        122460   \n",
      "228       229        Victorville            California        122385   \n",
      "229       230           Hartford           Connecticut        122105   \n",
      "230       231            Vallejo            California        121692   \n",
      "231       232          Allentown          Pennsylvania        121442   \n",
      "232       233           Berkeley            California        121363   \n",
      "233       234         Richardson                 Texas        121323   \n",
      "234       235             Arvada              Colorado        121272   \n",
      "235       236          Ann Arbor              Michigan        119980   \n",
      "236       237          Rochester             Minnesota        118935   \n",
      "237       238          Cambridge         Massachusetts        118927   \n",
      "238       239         Sugar Land                 Texas        118488   \n",
      "239       240            Lansing              Michigan        118210   \n",
      "240       241         Evansville               Indiana        117979   \n",
      "241       242    College Station                 Texas        117911   \n",
      "242       243          Fairfield            California        117133   \n",
      "243       244         Clearwater               Florida        116946   \n",
      "244       245           Beaumont                 Texas        116825   \n",
      "245       246       Independence              Missouri        116672   \n",
      "246       247              Provo                  Utah        116618   \n",
      "247       248        West Jordan                  Utah        116480   \n",
      "248       249           Murrieta            California        116223   \n",
      "249       250           Palm Bay               Florida        115552   \n",
      "250       251           El Monte            California        115487   \n",
      "251       252           Carlsbad            California        115382   \n",
      "252       252   North Charleston        South Carolina        115382   \n",
      "253       254           Temecula            California        114761   \n",
      "254       255             Clovis            California        114584   \n",
      "255       256        Springfield              Illinois        114230   \n",
      "256       257           Meridian                 Idaho        114161   \n",
      "257       258        Westminster              Colorado        113166   \n",
      "258       259         Costa Mesa            California        113003   \n",
      "259       260         High Point        North Carolina        112791   \n",
      "260       261         Manchester         New Hampshire        112673   \n",
      "261       262             Pueblo              Colorado        112361   \n",
      "262       263           Lakeland               Florida        112136   \n",
      "263       264      Pompano Beach               Florida        112118   \n",
      "264       265    West Palm Beach               Florida        111955   \n",
      "265       266            Antioch            California        111502   \n",
      "266       267            Everett            Washington        111475   \n",
      "267       268             Downey            California        111126   \n",
      "268       269             Lowell         Massachusetts        110997   \n",
      "269       270         Centennial              Colorado        110937   \n",
      "270       271              Elgin              Illinois        110849   \n",
      "271       272           Richmond            California        110567   \n",
      "272       273             Peoria              Illinois        110417   \n",
      "273       274       Broken Arrow              Oklahoma        110198   \n",
      "274       275      Miami Gardens               Florida        110001   \n",
      "275       276           Billings               Montana        109577   \n",
      "276       277      Jurupa Valley            California        109527   \n",
      "277       278      Sandy Springs               Georgia        109452   \n",
      "278       279            Gresham                Oregon        109381   \n",
      "279       280         Lewisville                 Texas        109212   \n",
      "280       281          Hillsboro                Oregon        109128   \n",
      "281       282        Ventura[ab]            California        109106   \n",
      "282       283            Greeley              Colorado        108649   \n",
      "283       284          Inglewood            California        108151   \n",
      "284       285          Waterbury           Connecticut        107568   \n",
      "285       286        League City                 Texas        107536   \n",
      "286       287        Santa Maria            California        107263   \n",
      "287       288              Tyler                 Texas        106985   \n",
      "288       289          Davie[ac]               Florida        106306   \n",
      "289       290       Lakewood[ad]            New Jersey        106300   \n",
      "290       291          Daly City            California        106280   \n",
      "291       292            Boulder              Colorado        105673   \n",
      "292       293              Allen                 Texas        105623   \n",
      "293       294        West Covina            California        105101   \n",
      "294       295             Sparks                Nevada        105006   \n",
      "295       296      Wichita Falls                 Texas        104683   \n",
      "296       297          Green Bay             Wisconsin        104578   \n",
      "297       298          San Mateo            California        104430   \n",
      "298       299            Norwalk            California        103949   \n",
      "299       300             Rialto            California        103526   \n",
      "300       301         Las Cruces            New Mexico        103432   \n",
      "301       302              Chico            California        103301   \n",
      "302       303           El Cajon            California        102708   \n",
      "303       304            Burbank            California        102511   \n",
      "304       305         South Bend               Indiana        102026   \n",
      "305       306             Renton            Washington        101751   \n",
      "306       307              Vista            California        101638   \n",
      "307       308          Davenport                  Iowa        101590   \n",
      "308       309           Edinburg                 Texas        101170   \n",
      "309       310         Tuscaloosa               Alabama        101129   \n",
      "310       311             Carmel               Indiana        101068   \n",
      "311       312     Spokane Valley            Washington        101060   \n",
      "312       313         San Angelo                 Texas        101004   \n",
      "313       314          Vacaville            California        100670   \n",
      "314       315        Clinton[ae]              Michigan        100471   \n",
      "315       316               Bend                Oregon        100421   \n",
      "316       317     Woodbridge[ad]            New Jersey        100145   \n",
      "\n",
      "     2010Census   Change 2016 land area 2016 land area.1  \\\n",
      "0       8175133   +1.98%    301.5 sq mi        780.9 km2   \n",
      "1       3792621   +4.93%    468.7 sq mi      1,213.9 km2   \n",
      "2       2695598   −0.06%    227.3 sq mi        588.7 km2   \n",
      "3       2100263  +10.48%    637.5 sq mi      1,651.1 km2   \n",
      "4       1445632  +16.28%    517.6 sq mi      1,340.6 km2   \n",
      "5       1526006   +3.80%    134.2 sq mi        347.6 km2   \n",
      "6       1327407  +16.56%    461.0 sq mi      1,194.0 km2   \n",
      "7       1307402   +8.91%    325.2 sq mi        842.3 km2   \n",
      "8       1197816  +12.17%    340.9 sq mi        882.9 km2   \n",
      "9        945942   +8.02%    177.5 sq mi        459.7 km2   \n",
      "10       790390  +23.85%    312.7 sq mi        809.9 km2   \n",
      "11       821784  +10.92%    747.4 sq mi      1,935.8 km2   \n",
      "12       741206  +22.72%    342.9 sq mi        888.1 km2   \n",
      "13       787033  +14.17%    218.5 sq mi        565.9 km2   \n",
      "14       731424  +21.09%    305.4 sq mi        791.0 km2   \n",
      "15       805235   +9.48%     46.9 sq mi        121.5 km2   \n",
      "16       820445   +6.82%    361.5 sq mi        936.3 km2   \n",
      "17       608660  +23.83%     83.8 sq mi        217.0 km2   \n",
      "18       600158  +21.17%    153.3 sq mi        397.0 km2   \n",
      "19       601723  +17.29%     61.1 sq mi        158.2 km2   \n",
      "20       617594  +12.14%     48.3 sq mi        125.1 km2   \n",
      "21       649121   +5.02%    256.8 sq mi        665.1 km2   \n",
      "22       601222  +11.58%    475.9 sq mi      1,232.6 km2   \n",
      "23       713777   −6.13%    138.8 sq mi        359.5 km2   \n",
      "24       579999  +12.94%    606.3 sq mi      1,570.3 km2   \n",
      "25       583776  +12.16%    133.5 sq mi        345.8 km2   \n",
      "26       583756  +11.57%    134.4 sq mi        348.1 km2   \n",
      "27       646889   +0.65%    317.4 sq mi        822.1 km2   \n",
      "28       597337   +3.40%    263.5 sq mi        682.5 km2   \n",
      "29       620961   −4.42%     80.9 sq mi        209.5 km2   \n",
      "30       594833   −0.79%     96.2 sq mi        249.2 km2   \n",
      "31       545852   +2.69%    188.2 sq mi        487.4 km2   \n",
      "32       520116   +5.38%    230.8 sq mi        597.8 km2   \n",
      "33       494665   +7.46%    114.4 sq mi        296.3 km2   \n",
      "34       439041  +17.99%    137.9 sq mi        357.2 km2   \n",
      "35       466488  +10.10%     97.9 sq mi        253.6 km2   \n",
      "36       420003  +20.67%    133.5 sq mi        345.8 km2   \n",
      "37       459787   +7.73%    315.0 sq mi        815.8 km2   \n",
      "38       416427  +14.84%    195.6 sq mi        506.6 km2   \n",
      "39       408958  +16.93%    133.2 sq mi        345.0 km2   \n",
      "40       403892  +17.38%    145.1 sq mi        375.8 km2   \n",
      "41       399457  +17.15%     36.0 sq mi         93.2 km2   \n",
      "42       462257   +0.08%     50.3 sq mi        130.3 km2   \n",
      "43       437994   +2.74%    244.7 sq mi        633.8 km2   \n",
      "44       390724  +10.83%     55.9 sq mi        144.8 km2   \n",
      "45       382578  +12.29%     54.0 sq mi        139.9 km2   \n",
      "46       391906   +2.37%    196.8 sq mi        509.7 km2   \n",
      "47       335709  +19.06%    113.4 sq mi        293.7 km2   \n",
      "48       365438   +9.14%     95.8 sq mi        248.1 km2   \n",
      "49       343829  +13.47%    169.4 sq mi        438.7 km2   \n",
      "50       382368   +1.98%    160.4 sq mi        415.4 km2   \n",
      "51       347483  +10.55%    148.8 sq mi        385.4 km2   \n",
      "52       396815   −3.98%     77.7 sq mi        201.2 km2   \n",
      "53       325078  +16.68%    153.5 sq mi        397.6 km2   \n",
      "54       336265   +4.19%     50.0 sq mi        129.5 km2   \n",
      "55       337256   +2.32%     60.5 sq mi        156.7 km2   \n",
      "56       324528   +2.40%     27.1 sq mi         70.2 km2   \n",
      "57       303871   +9.05%     81.2 sq mi        210.3 km2   \n",
      "58       305215   +7.00%    174.6 sq mi        452.2 km2   \n",
      "59       295803   +9.25%    283.6 sq mi        734.5 km2   \n",
      "60       257729  +24.23%    104.7 sq mi        271.2 km2   \n",
      "61       291707   +7.20%     61.7 sq mi        159.8 km2   \n",
      "62       285068   +8.08%     52.0 sq mi        134.7 km2   \n",
      "63       296943   +2.36%     77.4 sq mi        200.5 km2   \n",
      "64       319294   −5.86%     62.0 sq mi        160.6 km2   \n",
      "65       305704   −1.77%     55.4 sq mi        143.5 km2   \n",
      "66       269666  +10.03%    128.3 sq mi        332.3 km2   \n",
      "67       258379  +11.89%     92.1 sq mi        238.5 km2   \n",
      "68       291826   −1.31%  1,706.6 sq mi      4,420.1 km2   \n",
      "69       259841  +10.71%     71.7 sq mi        185.7 km2   \n",
      "70       238300  +20.62%    105.2 sq mi        272.5 km2   \n",
      "71       212375  +35.33%     65.6 sq mi        169.9 km2   \n",
      "72       277140   +1.76%     24.1 sq mi         62.4 km2   \n",
      "73       228330  +22.19%    109.8 sq mi        284.4 km2   \n",
      "74       243916  +12.54%     49.6 sq mi        128.5 km2   \n",
      "75       287208   −5.02%     80.7 sq mi        209.0 km2   \n",
      "76       253691   +6.59%    110.6 sq mi        286.5 km2   \n",
      "77       244769   +8.41%     61.8 sq mi        160.1 km2   \n",
      "78       236091  +11.18%    101.1 sq mi        261.8 km2   \n",
      "79       247597   +5.85%     14.8 sq mi         38.3 km2   \n",
      "80       236123  +10.61%     64.9 sq mi        168.1 km2   \n",
      "81       233209  +11.35%     77.0 sq mi        199.4 km2   \n",
      "82       229573  +12.76%    124.6 sq mi        322.7 km2   \n",
      "83       217385  +18.72%    183.9 sq mi        476.3 km2   \n",
      "84       225221  +13.49%    107.3 sq mi        277.9 km2   \n",
      "85       261310   −2.31%     40.4 sq mi        104.6 km2   \n",
      "86       208453  +21.90%     68.0 sq mi        176.1 km2   \n",
      "87       226721  +11.32%     59.1 sq mi        153.1 km2   \n",
      "88       216961  +16.14%     98.0 sq mi        253.8 km2   \n",
      "89       229617   +7.98%    132.5 sq mi        343.2 km2   \n",
      "90       222209  +10.18%    338.5 sq mi        876.7 km2   \n",
      "91       242803   −0.03%     53.3 sq mi        138.0 km2   \n",
      "92       214089  +12.62%     77.5 sq mi        200.7 km2   \n",
      "93       226876   +5.75%     57.0 sq mi        147.6 km2   \n",
      "94       216290  +10.87%     67.0 sq mi        173.5 km2   \n",
      "95       224669   +3.86%     21.5 sq mi         55.7 km2   \n",
      "96       204214  +12.84%     59.8 sq mi        154.9 km2   \n",
      "97       205671  +11.32%     82.1 sq mi        212.6 km2   \n",
      "98       208916   +6.30%     68.7 sq mi        177.9 km2   \n",
      "99       229493   −4.03%     85.9 sq mi        222.5 km2   \n",
      "100      198397   +9.79%     49.7 sq mi        128.7 km2   \n",
      "101      209924   +2.79%     61.5 sq mi        159.3 km2   \n",
      "102      201165   +6.97%     43.0 sq mi        111.4 km2   \n",
      "103      196069   +9.42%     43.0 sq mi        111.4 km2   \n",
      "104      203433   +5.31%     88.9 sq mi        230.2 km2   \n",
      "105      193365  +10.18%     51.3 sq mi        132.9 km2   \n",
      "106      176320  +20.79%     52.8 sq mi        136.8 km2   \n",
      "107      200564   +5.53%    147.7 sq mi        382.5 km2   \n",
      "108      212237   −1.34%    146.1 sq mi        378.4 km2   \n",
      "109      197899   +5.55%     26.9 sq mi         69.7 km2   \n",
      "110      210565   −2.31%     35.8 sq mi         92.7 km2   \n",
      "111      164603  +22.63%    118.9 sq mi        307.9 km2   \n",
      "112      188040   +6.90%     44.4 sq mi        115.0 km2   \n",
      "113      180105  +11.37%    213.4 sq mi        552.7 km2   \n",
      "114      186440   +7.58%    111.2 sq mi        288.0 km2   \n",
      "115      116989  +71.38%     67.7 sq mi        175.3 km2   \n",
      "116      195976   +2.24%     18.0 sq mi         46.6 km2   \n",
      "117      190695   +4.55%    101.4 sq mi        262.6 km2   \n",
      "118      191719   +3.96%     30.4 sq mi         78.7 km2   \n",
      "119      189992   +4.86%     26.9 sq mi         69.7 km2   \n",
      "120      131117  +51.91%     63.0 sq mi        163.2 km2   \n",
      "121      205764   −3.52%    159.8 sq mi        413.9 km2   \n",
      "122      195844   +1.04%    302.5 sq mi        783.5 km2   \n",
      "123      197899   −0.07%     44.9 sq mi        116.3 km2   \n",
      "124      199110   −0.76%     62.0 sq mi        160.6 km2   \n",
      "125      193524   +1.96%    118.7 sq mi        307.4 km2   \n",
      "126      161719  +21.08%     40.0 sq mi        103.6 km2   \n",
      "127      189885   +3.10%    216.4 sq mi        560.5 km2   \n",
      "128      173372  +12.76%     75.1 sq mi        194.5 km2   \n",
      "129      175396  +10.92%     72.3 sq mi        187.3 km2   \n",
      "130      181376   +7.24%    100.4 sq mi        260.0 km2   \n",
      "131      154305  +26.05%    105.6 sq mi        273.5 km2   \n",
      "132      195111   −3.28%    139.4 sq mi        361.0 km2   \n",
      "133      178874   +4.88%     98.5 sq mi        255.1 km2   \n",
      "134      199311   −6.12%    107.1 sq mi        277.4 km2   \n",
      "135      181045   +2.42%     37.4 sq mi         96.9 km2   \n",
      "136      163924  +12.86%     49.9 sq mi        129.2 km2   \n",
      "137      161791  +14.01%     46.9 sq mi        121.5 km2   \n",
      "138      153888  +19.43%     75.4 sq mi        195.3 km2   \n",
      "139      167674   +9.02%    143.1 sq mi        370.6 km2   \n",
      "140      175023   +4.43%    132.5 sq mi        343.2 km2   \n",
      "141      165521  +10.22%     34.6 sq mi         89.6 km2   \n",
      "142      178042   +1.03%     18.4 sq mi         47.7 km2   \n",
      "143      180719   −0.83%     69.1 sq mi        179.0 km2   \n",
      "144      165269   +7.46%     40.0 sq mi        103.6 km2   \n",
      "145      167815   +5.33%     41.3 sq mi        107.0 km2   \n",
      "146      154065  +14.21%    175.7 sq mi        455.1 km2   \n",
      "147      167086   +5.18%     41.3 sq mi        107.0 km2   \n",
      "148      153015  +14.22%     42.2 sq mi        109.3 km2   \n",
      "149      154637  +12.76%     48.6 sq mi        125.9 km2   \n",
      "150      154019  +12.71%     33.0 sq mi         85.5 km2   \n",
      "151      156185  +10.52%     44.1 sq mi        114.2 km2   \n",
      "152      170883   +0.45%     18.0 sq mi         46.6 km2   \n",
      "153      135234  +25.92%     56.5 sq mi        146.3 km2   \n",
      "154      143986  +18.24%     55.8 sq mi        144.5 km2   \n",
      "155      152374  +11.48%     39.5 sq mi        102.3 km2   \n",
      "156      159498   +5.26%     82.3 sq mi        213.2 km2   \n",
      "157      173514   −7.43%    111.0 sq mi        287.5 km2   \n",
      "158      139966  +13.90%     15.0 sq mi         38.8 km2   \n",
      "159      144186  +10.42%     45.5 sq mi        117.8 km2   \n",
      "160      132929  +18.97%     98.3 sq mi        254.6 km2   \n",
      "161      142980  +10.46%     42.9 sq mi        111.1 km2   \n",
      "162      156633   +0.62%     94.3 sq mi        244.2 km2   \n",
      "163      150441   +3.34%     23.6 sq mi         61.1 km2   \n",
      "164      152750   +1.52%    106.0 sq mi        274.5 km2   \n",
      "165      140768   +9.98%     27.3 sq mi         70.7 km2   \n",
      "166      153060   +0.36%     31.9 sq mi         82.6 km2   \n",
      "167       91351  +67.66%    249.3 sq mi        645.7 km2   \n",
      "168      145786   +4.92%    124.8 sq mi        323.2 km2   \n",
      "169      140081   +9.01%     22.0 sq mi         57.0 km2   \n",
      "170      149058   +1.77%     23.0 sq mi         59.6 km2   \n",
      "171      127921  +18.56%     53.5 sq mi        138.6 km2   \n",
      "172      143911   +5.36%     37.1 sq mi         96.1 km2   \n",
      "173      149043   +1.47%     43.5 sq mi        112.7 km2   \n",
      "174      141853   +4.65%     38.7 sq mi        100.2 km2   \n",
      "175      122363  +21.09%     33.5 sq mi         86.8 km2   \n",
      "176      147433   −0.06%     64.4 sq mi        166.8 km2   \n",
      "177      108755  +35.07%     55.9 sq mi        144.8 km2   \n",
      "178      111147  +31.39%     74.4 sq mi        192.7 km2   \n",
      "179      152871   −4.75%     63.5 sq mi        164.5 km2   \n",
      "180      146199   −0.66%      8.4 sq mi         21.8 km2   \n",
      "181      136286   +6.00%    103.6 sq mi        268.3 km2   \n",
      "182      144229   +0.12%     16.1 sq mi         41.7 km2   \n",
      "183      145438   −1.27%     20.5 sq mi         53.1 km2   \n",
      "184      129877  +10.31%     58.4 sq mi        151.3 km2   \n",
      "185      145170   −1.96%     25.0 sq mi         64.7 km2   \n",
      "186      117517  +20.55%    107.9 sq mi        279.5 km2   \n",
      "187      113383  +24.83%     93.4 sq mi        241.9 km2   \n",
      "188      118788  +19.12%     43.0 sq mi        111.4 km2   \n",
      "189      118772  +19.11%     35.7 sq mi         92.5 km2   \n",
      "190      122041  +15.69%     29.4 sq mi         76.1 km2   \n",
      "191      137122   +2.85%     23.0 sq mi         59.6 km2   \n",
      "192      139824   +0.80%     47.2 sq mi        122.2 km2   \n",
      "193      125872  +11.66%     60.9 sq mi        157.7 km2   \n",
      "194      141527   −0.79%     55.7 sq mi        144.3 km2   \n",
      "195      119097  +16.92%     36.3 sq mi         94.0 km2   \n",
      "196      124805  +11.56%     89.0 sq mi        230.5 km2   \n",
      "197      136416   +1.65%     25.4 sq mi         65.8 km2   \n",
      "198      135161   +2.57%     22.4 sq mi         58.0 km2   \n",
      "199      120083  +14.56%    109.0 sq mi        282.3 km2   \n",
      "200      129480   +4.45%     35.5 sq mi         91.9 km2   \n",
      "201      124442   +8.17%     37.5 sq mi         97.1 km2   \n",
      "202      137436   −2.13%     51.5 sq mi        133.4 km2   \n",
      "203      124354   +7.75%     62.3 sq mi        161.4 km2   \n",
      "204      134056   −0.08%     34.4 sq mi         89.1 km2   \n",
      "205      121096  +10.46%     23.8 sq mi         61.6 km2   \n",
      "206      126326   +5.73%     70.8 sq mi        183.4 km2   \n",
      "207       99887  +33.52%     35.6 sq mi         92.2 km2   \n",
      "208      129699   +2.11%     36.5 sq mi         94.5 km2   \n",
      "209       92411  +43.19%     33.7 sq mi         87.3 km2   \n",
      "210      129272   +1.86%    133.5 sq mi        345.8 km2   \n",
      "211      116468  +11.93%     18.4 sq mi         47.7 km2   \n",
      "212      129779   +0.36%     18.7 sq mi         48.4 km2   \n",
      "213      122643   +5.70%     37.6 sq mi         97.4 km2   \n",
      "214      122067   +5.92%     30.5 sq mi         79.0 km2   \n",
      "215      124969   +3.40%     12.3 sq mi         31.9 km2   \n",
      "216      115452   +9.93%    116.4 sq mi        301.5 km2   \n",
      "217      126683   +0.10%     55.2 sq mi        143.0 km2   \n",
      "218      120623   +4.61%     53.8 sq mi        139.3 km2   \n",
      "219      124237   +1.11%     41.5 sq mi        107.5 km2   \n",
      "220      127473   −1.70%     61.5 sq mi        159.3 km2   \n",
      "221      110925  +12.58%    178.8 sq mi        463.1 km2   \n",
      "222      105549  +18.11%     49.3 sq mi        127.7 km2   \n",
      "223      106476  +16.22%     51.6 sq mi        133.6 km2   \n",
      "224      117063   +5.43%    106.7 sq mi        276.4 km2   \n",
      "225       99940  +23.41%     45.2 sq mi        117.1 km2   \n",
      "226      108500  +13.54%     65.0 sq mi        168.3 km2   \n",
      "227       91252  +34.20%     46.3 sq mi        119.9 km2   \n",
      "228      115903   +5.59%     73.3 sq mi        189.8 km2   \n",
      "229      124775   −2.14%     17.4 sq mi         45.1 km2   \n",
      "230      115942   +4.96%     30.7 sq mi         79.5 km2   \n",
      "231      118032   +2.89%     17.5 sq mi         45.3 km2   \n",
      "232      112580   +7.80%     10.5 sq mi         27.2 km2   \n",
      "233       99223  +22.27%     28.6 sq mi         74.1 km2   \n",
      "234      106433  +13.94%     38.6 sq mi        100.0 km2   \n",
      "235      113934   +5.31%     28.1 sq mi         72.8 km2   \n",
      "236      106769  +11.39%     54.6 sq mi        141.4 km2   \n",
      "237      105162  +13.09%      6.4 sq mi         16.6 km2   \n",
      "238       78817  +50.33%     34.0 sq mi         88.1 km2   \n",
      "239      114297   +3.42%     39.1 sq mi        101.3 km2   \n",
      "240      117429   +0.47%     47.3 sq mi        122.5 km2   \n",
      "241       93857  +25.63%     51.0 sq mi        132.1 km2   \n",
      "242      105321  +11.22%     40.9 sq mi        105.9 km2   \n",
      "243      107685   +8.60%     25.9 sq mi         67.1 km2   \n",
      "244      118296   −1.24%     82.1 sq mi        212.6 km2   \n",
      "245      116830   −0.14%     77.8 sq mi        201.5 km2   \n",
      "246      112488   +3.67%     41.7 sq mi        108.0 km2   \n",
      "247      103712  +12.31%     32.3 sq mi         83.7 km2   \n",
      "248      103466  +12.33%     33.6 sq mi         87.0 km2   \n",
      "249      103190  +11.98%     65.7 sq mi        170.2 km2   \n",
      "250      113475   +1.77%      9.6 sq mi         24.9 km2   \n",
      "251      105328   +9.55%     37.7 sq mi         97.6 km2   \n",
      "252       97471  +18.38%     73.7 sq mi        190.9 km2   \n",
      "253      100097  +14.65%     37.3 sq mi         96.6 km2   \n",
      "254       95631  +19.82%     24.2 sq mi         62.7 km2   \n",
      "255      116250   −1.74%     60.1 sq mi        155.7 km2   \n",
      "256       75092  +52.03%     29.8 sq mi         77.2 km2   \n",
      "257      106114   +6.65%     31.7 sq mi         82.1 km2   \n",
      "258      109960   +2.77%     15.7 sq mi         40.7 km2   \n",
      "259      104371   +8.07%     55.2 sq mi        143.0 km2   \n",
      "260      109565   +2.84%     33.1 sq mi         85.7 km2   \n",
      "261      106595   +5.41%     53.6 sq mi        138.8 km2   \n",
      "262       97422  +15.10%     65.9 sq mi        170.7 km2   \n",
      "263       99845  +12.29%     24.0 sq mi         62.2 km2   \n",
      "264       99919  +12.05%     55.1 sq mi        142.7 km2   \n",
      "265      102372   +8.92%     29.4 sq mi         76.1 km2   \n",
      "266      103019   +8.21%     33.3 sq mi         86.2 km2   \n",
      "267      111772   −0.58%     12.4 sq mi         32.1 km2   \n",
      "268      106519   +4.20%     13.6 sq mi         35.2 km2   \n",
      "269      100377  +10.52%     29.5 sq mi         76.4 km2   \n",
      "270      108188   +2.46%     37.4 sq mi         96.9 km2   \n",
      "271      103701   +6.62%     30.1 sq mi         78.0 km2   \n",
      "272      115007   −3.99%     48.2 sq mi        124.8 km2   \n",
      "273       98850  +11.48%     61.7 sq mi        159.8 km2   \n",
      "274      107167   +2.64%     18.2 sq mi         47.1 km2   \n",
      "275      104170   +5.19%     43.7 sq mi        113.2 km2   \n",
      "276           0     [aa]     42.9 sq mi        111.1 km2   \n",
      "277       93853  +16.62%     37.7 sq mi         97.6 km2   \n",
      "278      105594   +3.59%     23.3 sq mi         60.3 km2   \n",
      "279       95290  +14.61%     36.7 sq mi         95.1 km2   \n",
      "280       91611  +19.12%     25.0 sq mi         64.7 km2   \n",
      "281      106433   +2.51%     21.8 sq mi         56.5 km2   \n",
      "282       92889  +16.97%     47.8 sq mi        123.8 km2   \n",
      "283      109673   −1.39%      9.1 sq mi         23.6 km2   \n",
      "284      110366   −2.54%     28.5 sq mi         73.8 km2   \n",
      "285       83560  +28.69%     51.2 sq mi        132.6 km2   \n",
      "286       99553   +7.74%     22.8 sq mi         59.1 km2   \n",
      "287       96900  +10.41%     56.6 sq mi        146.6 km2   \n",
      "288       91992  +15.56%     34.9 sq mi         90.4 km2   \n",
      "289       92843  +14.49%     24.7 sq mi         64.0 km2   \n",
      "290      101123   +5.10%      7.6 sq mi         19.7 km2   \n",
      "291       97385   +8.51%     24.8 sq mi         64.2 km2   \n",
      "292       84246  +25.37%     27.1 sq mi         70.2 km2   \n",
      "293      106098   −0.94%     16.0 sq mi         41.4 km2   \n",
      "294       90264  +16.33%     35.9 sq mi         93.0 km2   \n",
      "295      104553   +0.12%     72.2 sq mi        187.0 km2   \n",
      "296      104057   +0.50%     45.4 sq mi        117.6 km2   \n",
      "297       97207   +7.43%     12.1 sq mi         31.3 km2   \n",
      "298      105549   −1.52%      9.7 sq mi         25.1 km2   \n",
      "299       99171   +4.39%     22.3 sq mi         57.8 km2   \n",
      "300       97618   +5.96%     76.9 sq mi        199.2 km2   \n",
      "301       86187  +19.86%     33.3 sq mi         86.2 km2   \n",
      "302       99478   +3.25%     14.5 sq mi         37.6 km2   \n",
      "303      103340   −0.80%     17.4 sq mi         45.1 km2   \n",
      "304      101168   +0.85%     41.4 sq mi        107.2 km2   \n",
      "305       90927  +11.90%     23.4 sq mi         60.6 km2   \n",
      "306       93834   +8.32%     18.7 sq mi         48.4 km2   \n",
      "307       99685   +1.91%     62.9 sq mi        162.9 km2   \n",
      "308       77100  +31.22%     37.6 sq mi         97.4 km2   \n",
      "309       90468  +11.78%     71.7 sq mi        185.7 km2   \n",
      "310       79191  +27.63%     47.5 sq mi        123.0 km2   \n",
      "311       89755  +12.60%     37.7 sq mi         97.6 km2   \n",
      "312       93200   +8.37%     59.9 sq mi        155.1 km2   \n",
      "313       92428   +8.92%     29.0 sq mi         75.1 km2   \n",
      "314       96796   +3.80%     28.1 sq mi         72.8 km2   \n",
      "315       76639  +31.03%     33.1 sq mi         85.7 km2   \n",
      "316       99585   +0.56%     23.3 sq mi         60.3 km2   \n",
      "\n",
      "    2016 population density 2016 population density.1  \\\n",
      "0              28,317/sq mi                10,933/km2   \n",
      "1               8,484/sq mi                 3,276/km2   \n",
      "2              11,900/sq mi                 4,600/km2   \n",
      "3               3,613/sq mi                 1,395/km2   \n",
      "4               3,120/sq mi                 1,200/km2   \n",
      "5              11,683/sq mi                 4,511/km2   \n",
      "6               3,238/sq mi                 1,250/km2   \n",
      "7               4,325/sq mi                 1,670/km2   \n",
      "8               3,866/sq mi                 1,493/km2   \n",
      "9               5,777/sq mi                 2,231/km2   \n",
      "10              3,031/sq mi                 1,170/km2   \n",
      "11              1,178/sq mi                   455/km2   \n",
      "12              2,491/sq mi                   962/km2   \n",
      "13              3,936/sq mi                 1,520/km2   \n",
      "14              2,757/sq mi                 1,064/km2   \n",
      "15             18,569/sq mi                 7,170/km2   \n",
      "16              2,366/sq mi                   914/km2   \n",
      "17              8,405/sq mi                 3,245/km2   \n",
      "18              4,521/sq mi                 1,746/km2   \n",
      "19             11,148/sq mi                 4,304/km2   \n",
      "20             13,938/sq mi                 5,381/km2   \n",
      "21              2,660/sq mi                 1,030/km2   \n",
      "22              1,388/sq mi                   536/km2   \n",
      "23              4,847/sq mi                 1,871/km2   \n",
      "24              1,053/sq mi                   407/km2   \n",
      "25              4,793/sq mi                 1,851/km2   \n",
      "26              4,709/sq mi                 1,818/km2   \n",
      "27              2,056/sq mi                   794/km2   \n",
      "28              2,339/sq mi                   903/km2   \n",
      "29              7,598/sq mi                 2,934/km2   \n",
      "30              6,186/sq mi                 2,388/km2   \n",
      "31              2,972/sq mi                 1,147/km2   \n",
      "32              2,299/sq mi                   888/km2   \n",
      "33              4,563/sq mi                 1,762/km2   \n",
      "34              3,514/sq mi                 1,357/km2   \n",
      "35              5,059/sq mi                 1,953/km2   \n",
      "36              3,539/sq mi                 1,366/km2   \n",
      "37              1,528/sq mi                   590/km2   \n",
      "38              2,378/sq mi                   918/km2   \n",
      "39              3,356/sq mi                 1,296/km2   \n",
      "40              3,163/sq mi                 1,221/km2   \n",
      "41             12,599/sq mi                 4,865/km2   \n",
      "42              9,347/sq mi                 3,609/km2   \n",
      "43              1,850/sq mi                   710/km2   \n",
      "44              7,514/sq mi                 2,901/km2   \n",
      "45              7,660/sq mi                 2,960/km2   \n",
      "46              2,048/sq mi                   791/km2   \n",
      "47              3,326/sq mi                 1,284/km2   \n",
      "48              4,100/sq mi                 1,600/km2   \n",
      "49              2,311/sq mi                   892/km2   \n",
      "50              2,431/sq mi                   939/km2   \n",
      "51              2,529/sq mi                   976/km2   \n",
      "52              4,965/sq mi                 1,917/km2   \n",
      "53              2,356/sq mi                   910/km2   \n",
      "54              7,021/sq mi                 2,711/km2   \n",
      "55              5,815/sq mi                 2,245/km2   \n",
      "56             12,333/sq mi                 4,762/km2   \n",
      "57              3,999/sq mi                 1,544/km2   \n",
      "58              1,866/sq mi                   720/km2   \n",
      "59              1,123/sq mi                   434/km2   \n",
      "60              2,798/sq mi                 1,080/km2   \n",
      "61              4,977/sq mi                 1,922/km2   \n",
      "62              5,815/sq mi                 2,245/km2   \n",
      "63              3,860/sq mi                 1,490/km2   \n",
      "64              5,023/sq mi                 1,939/km2   \n",
      "65              5,481/sq mi                 2,116/km2   \n",
      "66              2,237/sq mi                   864/km2   \n",
      "67              3,044/sq mi                 1,175/km2   \n",
      "68                175/sq mi                    68/km2   \n",
      "69              3,990/sq mi                 1,540/km2   \n",
      "70              2,635/sq mi                 1,017/km2   \n",
      "71              4,057/sq mi                 1,566/km2   \n",
      "72             11,691/sq mi                 4,514/km2   \n",
      "73              2,395/sq mi                   925/km2   \n",
      "74              5,387/sq mi                 2,080/km2   \n",
      "75              3,451/sq mi                 1,332/km2   \n",
      "76              2,391/sq mi                   923/km2   \n",
      "77              4,223/sq mi                 1,631/km2   \n",
      "78              2,544/sq mi                   982/km2   \n",
      "79             17,848/sq mi                 6,891/km2   \n",
      "80              3,813/sq mi                 1,472/km2   \n",
      "81              3,280/sq mi                 1,270/km2   \n",
      "82              2,027/sq mi                   783/km2   \n",
      "83              1,341/sq mi                   518/km2   \n",
      "84              2,286/sq mi                   883/km2   \n",
      "85              6,359/sq mi                 2,455/km2   \n",
      "86              3,487/sq mi                 1,346/km2   \n",
      "87              4,161/sq mi                 1,607/km2   \n",
      "88              2,436/sq mi                   941/km2   \n",
      "89              1,828/sq mi                   706/km2   \n",
      "90                703/sq mi                   271/km2   \n",
      "91              4,599/sq mi                 1,776/km2   \n",
      "92              3,008/sq mi                 1,161/km2   \n",
      "93              4,122/sq mi                 1,592/km2   \n",
      "94              3,557/sq mi                 1,373/km2   \n",
      "95             10,995/sq mi                 4,245/km2   \n",
      "96              3,732/sq mi                 1,441/km2   \n",
      "97              2,718/sq mi                 1,049/km2   \n",
      "98              3,144/sq mi                 1,214/km2   \n",
      "99              2,651/sq mi                 1,024/km2   \n",
      "100             4,251/sq mi                 1,641/km2   \n",
      "101             3,516/sq mi                 1,358/km2   \n",
      "102             4,934/sq mi                 1,905/km2   \n",
      "103             4,876/sq mi                 1,883/km2   \n",
      "104             2,424/sq mi                   936/km2   \n",
      "105             4,006/sq mi                 1,547/km2   \n",
      "106             3,446/sq mi                 1,331/km2   \n",
      "107             1,386/sq mi                   535/km2   \n",
      "108             1,452/sq mi                   561/km2   \n",
      "109             7,729/sq mi                 2,984/km2   \n",
      "110             5,835/sq mi                 2,253/km2   \n",
      "111             1,557/sq mi                   601/km2   \n",
      "112             4,424/sq mi                 1,708/km2   \n",
      "113               905/sq mi                   349/km2   \n",
      "114             1,742/sq mi                   673/km2   \n",
      "115             2,417/sq mi                   933/km2   \n",
      "116            11,156/sq mi                 4,307/km2   \n",
      "117             1,968/sq mi                   760/km2   \n",
      "118             6,606/sq mi                 2,551/km2   \n",
      "119             7,459/sq mi                 2,880/km2   \n",
      "120             2,735/sq mi                 1,056/km2   \n",
      "121             1,252/sq mi                   483/km2   \n",
      "122               652/sq mi                   252/km2   \n",
      "123             4,479/sq mi                 1,729/km2   \n",
      "124             3,188/sq mi                 1,231/km2   \n",
      "125             1,673/sq mi                   646/km2   \n",
      "126             4,562/sq mi                 1,761/km2   \n",
      "127               913/sq mi                   353/km2   \n",
      "128             2,516/sq mi                   971/km2   \n",
      "129             2,637/sq mi                 1,018/km2   \n",
      "130             1,901/sq mi                   734/km2   \n",
      "131             1,703/sq mi                   658/km2   \n",
      "132             1,384/sq mi                   534/km2   \n",
      "133             1,891/sq mi                   730/km2   \n",
      "134             1,820/sq mi                   700/km2   \n",
      "135             4,933/sq mi                 1,905/km2   \n",
      "136             3,471/sq mi                 1,340/km2   \n",
      "137             3,728/sq mi                 1,439/km2   \n",
      "138             2,312/sq mi                   893/km2   \n",
      "139             1,241/sq mi                   479/km2   \n",
      "140             1,387/sq mi                   536/km2   \n",
      "141             5,166/sq mi                 1,995/km2   \n",
      "142             9,740/sq mi                 3,760/km2   \n",
      "143             2,631/sq mi                 1,016/km2   \n",
      "144             4,413/sq mi                 1,704/km2   \n",
      "145             4,241/sq mi                 1,637/km2   \n",
      "146               934/sq mi                   361/km2   \n",
      "147             4,249/sq mi                 1,641/km2   \n",
      "148             4,022/sq mi                 1,553/km2   \n",
      "149             3,445/sq mi                 1,330/km2   \n",
      "150             5,109/sq mi                 1,973/km2   \n",
      "151             3,777/sq mi                 1,458/km2   \n",
      "152             9,714/sq mi                 3,751/km2   \n",
      "153             2,873/sq mi                 1,109/km2   \n",
      "154             2,943/sq mi                 1,136/km2   \n",
      "155             4,222/sq mi                 1,630/km2   \n",
      "156             2,033/sq mi                   785/km2   \n",
      "157             1,524/sq mi                   588/km2   \n",
      "158            10,387/sq mi                 4,010/km2   \n",
      "159             3,493/sq mi                 1,349/km2   \n",
      "160             1,529/sq mi                   590/km2   \n",
      "161             3,599/sq mi                 1,390/km2   \n",
      "162             1,698/sq mi                   656/km2   \n",
      "163             6,662/sq mi                 2,572/km2   \n",
      "164             1,484/sq mi                   573/km2   \n",
      "165             5,568/sq mi                 2,150/km2   \n",
      "166             4,830/sq mi                 1,860/km2   \n",
      "167               612/sq mi                   236/km2   \n",
      "168             1,216/sq mi                   470/km2   \n",
      "169             6,944/sq mi                 2,681/km2   \n",
      "170             6,630/sq mi                 2,560/km2   \n",
      "171             2,680/sq mi                 1,030/km2   \n",
      "172             4,087/sq mi                 1,578/km2   \n",
      "173             3,525/sq mi                 1,361/km2   \n",
      "174             3,802/sq mi                 1,468/km2   \n",
      "175             4,221/sq mi                 1,630/km2   \n",
      "176             2,302/sq mi                   889/km2   \n",
      "177             2,360/sq mi                   910/km2   \n",
      "178             1,809/sq mi                   698/km2   \n",
      "179             2,325/sq mi                   898/km2   \n",
      "180            17,500/sq mi                 6,800/km2   \n",
      "181             1,417/sq mi                   547/km2   \n",
      "182             9,064/sq mi                 3,500/km2   \n",
      "183             7,180/sq mi                 2,770/km2   \n",
      "184             2,435/sq mi                   940/km2   \n",
      "185             5,735/sq mi                 2,214/km2   \n",
      "186             1,230/sq mi                   470/km2   \n",
      "187             1,433/sq mi                   553/km2   \n",
      "188             3,085/sq mi                 1,191/km2   \n",
      "189             3,829/sq mi                 1,478/km2   \n",
      "190             4,709/sq mi                 1,818/km2   \n",
      "191             6,176/sq mi                 2,385/km2   \n",
      "192             3,045/sq mi                 1,176/km2   \n",
      "193             2,225/sq mi                   859/km2   \n",
      "194             2,522/sq mi                   974/km2   \n",
      "195             3,674/sq mi                 1,419/km2   \n",
      "196             1,510/sq mi                   580/km2   \n",
      "197             5,532/sq mi                 2,136/km2   \n",
      "198             6,282/sq mi                 2,425/km2   \n",
      "199             1,233/sq mi                   476/km2   \n",
      "200             3,847/sq mi                 1,485/km2   \n",
      "201             3,495/sq mi                 1,349/km2   \n",
      "202             2,629/sq mi                 1,015/km2   \n",
      "203             2,112/sq mi                   815/km2   \n",
      "204             3,928/sq mi                 1,517/km2   \n",
      "205             5,465/sq mi                 2,110/km2   \n",
      "206             1,852/sq mi                   715/km2   \n",
      "207             3,396/sq mi                 1,311/km2   \n",
      "208             3,628/sq mi                 1,401/km2   \n",
      "209             3,784/sq mi                 1,461/km2   \n",
      "210             1,006/sq mi                   388/km2   \n",
      "211             6,845/sq mi                 2,643/km2   \n",
      "212             6,948/sq mi                 2,683/km2   \n",
      "213             3,434/sq mi                 1,326/km2   \n",
      "214             4,221/sq mi                 1,630/km2   \n",
      "215            10,459/sq mi                 4,038/km2   \n",
      "216             1,060/sq mi                   410/km2   \n",
      "217             2,335/sq mi                   902/km2   \n",
      "218             2,372/sq mi                   916/km2   \n",
      "219             3,044/sq mi                 1,175/km2   \n",
      "220             2,062/sq mi                   796/km2   \n",
      "221               683/sq mi                   264/km2   \n",
      "222             2,450/sq mi                   950/km2   \n",
      "223             2,278/sq mi                   880/km2   \n",
      "224             1,146/sq mi                   442/km2   \n",
      "225             2,608/sq mi                 1,007/km2   \n",
      "226             1,856/sq mi                   717/km2   \n",
      "227             2,453/sq mi                   947/km2   \n",
      "228             1,668/sq mi                   644/km2   \n",
      "229             7,083/sq mi                 2,735/km2   \n",
      "230             3,951/sq mi                 1,525/km2   \n",
      "231             6,882/sq mi                 2,657/km2   \n",
      "232            11,547/sq mi                 4,458/km2   \n",
      "233             3,963/sq mi                 1,530/km2   \n",
      "234             3,043/sq mi                 1,175/km2   \n",
      "235             4,298/sq mi                 1,659/km2   \n",
      "236             2,088/sq mi                   806/km2   \n",
      "237            17,289/sq mi                 6,675/km2   \n",
      "238             3,488/sq mi                 1,347/km2   \n",
      "239             2,967/sq mi                 1,146/km2   \n",
      "240             2,526/sq mi                   975/km2   \n",
      "241             2,199/sq mi                   849/km2   \n",
      "242             2,806/sq mi                 1,083/km2   \n",
      "243             4,415/sq mi                 1,705/km2   \n",
      "244             1,441/sq mi                   556/km2   \n",
      "245             1,504/sq mi                   581/km2   \n",
      "246             2,803/sq mi                 1,082/km2   \n",
      "247             3,520/sq mi                 1,360/km2   \n",
      "248             3,324/sq mi                 1,283/km2   \n",
      "249             1,676/sq mi                   647/km2   \n",
      "250            12,063/sq mi                 4,658/km2   \n",
      "251             3,023/sq mi                 1,167/km2   \n",
      "252             1,483/sq mi                   573/km2   \n",
      "253             3,031/sq mi                 1,170/km2   \n",
      "254             4,404/sq mi                 1,700/km2   \n",
      "255             1,925/sq mi                   743/km2   \n",
      "256             3,584/sq mi                 1,384/km2   \n",
      "257             3,592/sq mi                 1,387/km2   \n",
      "258             7,186/sq mi                 2,775/km2   \n",
      "259             2,015/sq mi                   778/km2   \n",
      "260             3,339/sq mi                 1,289/km2   \n",
      "261             2,058/sq mi                   795/km2   \n",
      "262             1,615/sq mi                   624/km2   \n",
      "263             4,558/sq mi                 1,760/km2   \n",
      "264             1,963/sq mi                   758/km2   \n",
      "265             3,772/sq mi                 1,456/km2   \n",
      "266             3,275/sq mi                 1,264/km2   \n",
      "267             9,134/sq mi                 3,527/km2   \n",
      "268             8,129/sq mi                 3,139/km2   \n",
      "269             3,727/sq mi                 1,439/km2   \n",
      "270             2,998/sq mi                 1,158/km2   \n",
      "271             3,648/sq mi                 1,409/km2   \n",
      "272             2,371/sq mi                   915/km2   \n",
      "273             1,741/sq mi                   672/km2   \n",
      "274             6,212/sq mi                 2,398/km2   \n",
      "275             2,525/sq mi                   975/km2   \n",
      "276             2,414/sq mi                   932/km2   \n",
      "277             2,804/sq mi                 1,083/km2   \n",
      "278             4,786/sq mi                 1,848/km2   \n",
      "279             2,852/sq mi                 1,101/km2   \n",
      "280             4,207/sq mi                 1,624/km2   \n",
      "281             5,027/sq mi                 1,941/km2   \n",
      "282             2,176/sq mi                   840/km2   \n",
      "283            12,160/sq mi                 4,700/km2   \n",
      "284             3,799/sq mi                 1,467/km2   \n",
      "285             1,992/sq mi                   769/km2   \n",
      "286             4,662/sq mi                 1,800/km2   \n",
      "287             1,852/sq mi                   715/km2   \n",
      "288             2,919/sq mi                 1,127/km2   \n",
      "289             4,079/sq mi                 1,575/km2   \n",
      "290            14,009/sq mi                 5,409/km2   \n",
      "291             4,358/sq mi                 1,683/km2   \n",
      "292             3,715/sq mi                 1,434/km2   \n",
      "293             6,740/sq mi                 2,600/km2   \n",
      "294             2,810/sq mi                 1,080/km2   \n",
      "295             1,450/sq mi                   560/km2   \n",
      "296             2,316/sq mi                   894/km2   \n",
      "297             8,592/sq mi                 3,317/km2   \n",
      "298            10,946/sq mi                 4,226/km2   \n",
      "299             4,633/sq mi                 1,789/km2   \n",
      "300             1,323/sq mi                   511/km2   \n",
      "301             3,102/sq mi                 1,198/km2   \n",
      "302             7,156/sq mi                 2,763/km2   \n",
      "303             6,003/sq mi                 2,318/km2   \n",
      "304             2,457/sq mi                   949/km2   \n",
      "305             4,314/sq mi                 1,666/km2   \n",
      "306             5,436/sq mi                 2,099/km2   \n",
      "307             1,631/sq mi                   630/km2   \n",
      "308             2,691/sq mi                 1,039/km2   \n",
      "309             1,399/sq mi                   540/km2   \n",
      "310             2,128/sq mi                   822/km2   \n",
      "311             2,681/sq mi                 1,035/km2   \n",
      "312             1,681/sq mi                   649/km2   \n",
      "313             3,449/sq mi                 1,332/km2   \n",
      "314             3,573/sq mi                 1,380/km2   \n",
      "315             3,034/sq mi                 1,171/km2   \n",
      "316             4,351/sq mi                 1,680/km2   \n",
      "\n",
      "                                              Location  \n",
      "0        40°39′49″N 73°56′19″W﻿ / ﻿40.6635°N 73.9387°W  \n",
      "1      34°01′10″N 118°24′39″W﻿ / ﻿34.0194°N 118.4108°W  \n",
      "2        41°50′15″N 87°40′54″W﻿ / ﻿41.8376°N 87.6818°W  \n",
      "3        29°47′12″N 95°23′27″W﻿ / ﻿29.7866°N 95.3909°W  \n",
      "4      33°34′20″N 112°05′24″W﻿ / ﻿33.5722°N 112.0901°W  \n",
      "5        40°00′34″N 75°08′00″W﻿ / ﻿40.0094°N 75.1333°W  \n",
      "6        29°28′21″N 98°31′30″W﻿ / ﻿29.4724°N 98.5251°W  \n",
      "7      32°48′55″N 117°08′06″W﻿ / ﻿32.8153°N 117.1350°W  \n",
      "8        32°47′36″N 96°45′59″W﻿ / ﻿32.7933°N 96.7665°W  \n",
      "9      37°17′48″N 121°49′08″W﻿ / ﻿37.2967°N 121.8189°W  \n",
      "10       30°18′14″N 97°45′16″W﻿ / ﻿30.3039°N 97.7544°W  \n",
      "11       30°20′13″N 81°39′42″W﻿ / ﻿30.3369°N 81.6616°W  \n",
      "12       32°46′53″N 97°20′48″W﻿ / ﻿32.7815°N 97.3467°W  \n",
      "13       39°59′07″N 82°59′05″W﻿ / ﻿39.9852°N 82.9848°W  \n",
      "14       35°12′28″N 80°49′52″W﻿ / ﻿35.2078°N 80.8310°W  \n",
      "15     37°43′38″N 123°01′56″W﻿ / ﻿37.7272°N 123.0322°W  \n",
      "16       39°46′36″N 86°08′45″W﻿ / ﻿39.7767°N 86.1459°W  \n",
      "17     47°37′14″N 122°21′03″W﻿ / ﻿47.6205°N 122.3509°W  \n",
      "18     39°45′43″N 104°52′52″W﻿ / ﻿39.7619°N 104.8811°W  \n",
      "19       38°54′15″N 77°01′02″W﻿ / ﻿38.9041°N 77.0172°W  \n",
      "20       42°19′55″N 71°01′13″W﻿ / ﻿42.3320°N 71.0202°W  \n",
      "21     31°50′54″N 106°25′37″W﻿ / ﻿31.8484°N 106.4270°W  \n",
      "22       36°10′18″N 86°47′06″W﻿ / ﻿36.1718°N 86.7850°W  \n",
      "23       42°22′59″N 83°06′08″W﻿ / ﻿42.3830°N 83.1022°W  \n",
      "24       35°28′02″N 97°30′49″W﻿ / ﻿35.4671°N 97.5137°W  \n",
      "25     45°32′13″N 122°39′00″W﻿ / ﻿45.5370°N 122.6500°W  \n",
      "26     36°13′45″N 115°15′36″W﻿ / ﻿36.2292°N 115.2601°W  \n",
      "27       35°06′10″N 89°58′39″W﻿ / ﻿35.1028°N 89.9774°W  \n",
      "28       38°09′55″N 85°38′51″W﻿ / ﻿38.1654°N 85.6474°W  \n",
      "29       39°18′00″N 76°36′38″W﻿ / ﻿39.3000°N 76.6105°W  \n",
      "30       43°03′48″N 87°58′00″W﻿ / ﻿43.0633°N 87.9667°W  \n",
      "31     35°06′20″N 106°38′51″W﻿ / ﻿35.1056°N 106.6474°W  \n",
      "32     32°09′11″N 110°52′14″W﻿ / ﻿32.1531°N 110.8706°W  \n",
      "33     36°47′01″N 119°47′36″W﻿ / ﻿36.7836°N 119.7934°W  \n",
      "34     33°24′07″N 111°43′03″W﻿ / ﻿33.4019°N 111.7174°W  \n",
      "35     38°34′00″N 121°28′07″W﻿ / ﻿38.5666°N 121.4686°W  \n",
      "36       33°45′46″N 84°25′22″W﻿ / ﻿33.7629°N 84.4227°W  \n",
      "37       39°07′30″N 94°33′04″W﻿ / ﻿39.1251°N 94.5510°W  \n",
      "38     38°52′02″N 104°45′39″W﻿ / ﻿38.8673°N 104.7607°W  \n",
      "39       41°15′52″N 96°02′42″W﻿ / ﻿41.2644°N 96.0451°W  \n",
      "40       35°49′50″N 78°38′30″W﻿ / ﻿35.8306°N 78.6418°W  \n",
      "41       25°46′31″N 80°12′31″W﻿ / ﻿25.7752°N 80.2086°W  \n",
      "42     33°48′33″N 118°09′19″W﻿ / ﻿33.8092°N 118.1553°W  \n",
      "43       36°46′48″N 76°01′31″W﻿ / ﻿36.7800°N 76.0252°W  \n",
      "44     37°46′11″N 122°13′33″W﻿ / ﻿37.7698°N 122.2257°W  \n",
      "45       44°57′48″N 93°16′06″W﻿ / ﻿44.9633°N 93.2683°W  \n",
      "46       36°07′40″N 95°54′08″W﻿ / ﻿36.1279°N 95.9023°W  \n",
      "47       27°58′12″N 82°28′47″W﻿ / ﻿27.9701°N 82.4797°W  \n",
      "48       32°42′03″N 97°07′29″W﻿ / ﻿32.7007°N 97.1247°W  \n",
      "49       30°03′12″N 89°56′04″W﻿ / ﻿30.0534°N 89.9345°W  \n",
      "50       37°41′27″N 97°20′45″W﻿ / ﻿37.6907°N 97.3459°W  \n",
      "51     35°19′16″N 119°01′06″W﻿ / ﻿35.3212°N 119.0183°W  \n",
      "52       41°28′43″N 81°40′46″W﻿ / ﻿41.4785°N 81.6794°W  \n",
      "53     39°41′17″N 104°41′23″W﻿ / ﻿39.6880°N 104.6897°W  \n",
      "54     33°51′20″N 117°45′36″W﻿ / ﻿33.8555°N 117.7601°W  \n",
      "55     21°19′27″N 157°50′51″W﻿ / ﻿21.3243°N 157.8476°W  \n",
      "56     33°44′11″N 117°52′59″W﻿ / ﻿33.7363°N 117.8830°W  \n",
      "57     33°56′17″N 117°23′36″W﻿ / ﻿33.9381°N 117.3932°W  \n",
      "58       27°45′15″N 97°10′24″W﻿ / ﻿27.7543°N 97.1734°W  \n",
      "59       38°02′27″N 84°27′30″W﻿ / ﻿38.0407°N 84.4583°W  \n",
      "60     36°00′35″N 115°02′09″W﻿ / ﻿36.0097°N 115.0357°W  \n",
      "61     37°58′35″N 121°18′48″W﻿ / ﻿37.9763°N 121.3133°W  \n",
      "62       44°56′56″N 93°06′15″W﻿ / ﻿44.9489°N 93.1041°W  \n",
      "63       39°08′25″N 84°30′21″W﻿ / ﻿39.1402°N 84.5058°W  \n",
      "64       38°38′09″N 90°14′41″W﻿ / ﻿38.6357°N 90.2446°W  \n",
      "65       40°26′23″N 79°58′36″W﻿ / ﻿40.4398°N 79.9766°W  \n",
      "66       36°05′42″N 79°49′37″W﻿ / ﻿36.0951°N 79.8270°W  \n",
      "67       40°48′38″N 96°40′49″W﻿ / ﻿40.8105°N 96.6803°W  \n",
      "68     61°10′27″N 149°17′03″W﻿ / ﻿61.1743°N 149.2843°W  \n",
      "69       33°03′03″N 96°44′52″W﻿ / ﻿33.0508°N 96.7479°W  \n",
      "70       28°25′00″N 81°16′25″W﻿ / ﻿28.4166°N 81.2736°W  \n",
      "71     33°40′42″N 117°46′17″W﻿ / ﻿33.6784°N 117.7713°W  \n",
      "72       40°43′27″N 74°10′21″W﻿ / ﻿40.7242°N 74.1726°W  \n",
      "73       35°58′52″N 78°54′10″W﻿ / ﻿35.9811°N 78.9029°W  \n",
      "74     32°37′40″N 117°00′55″W﻿ / ﻿32.6277°N 117.0152°W  \n",
      "75       41°39′51″N 83°34′55″W﻿ / ﻿41.6641°N 83.5819°W  \n",
      "76       41°05′18″N 85°08′38″W﻿ / ﻿41.0882°N 85.1439°W  \n",
      "77       27°45′43″N 82°38′39″W﻿ / ﻿27.7620°N 82.6441°W  \n",
      "78       27°33′37″N 99°29′21″W﻿ / ﻿27.5604°N 99.4892°W  \n",
      "79       40°42′41″N 74°03′53″W﻿ / ﻿40.7114°N 74.0648°W  \n",
      "80     33°16′58″N 111°51′18″W﻿ / ﻿33.2829°N 111.8549°W  \n",
      "81       43°05′16″N 89°25′48″W﻿ / ﻿43.0878°N 89.4299°W  \n",
      "82     33°33′56″N 101°53′12″W﻿ / ﻿33.5656°N 101.8867°W  \n",
      "83     33°41′03″N 111°51′40″W﻿ / ﻿33.6843°N 111.8611°W  \n",
      "84     39°32′57″N 119°51′00″W﻿ / ﻿39.5491°N 119.8499°W  \n",
      "85       42°53′33″N 78°51′35″W﻿ / ﻿42.8925°N 78.8597°W  \n",
      "86     33°18′37″N 111°44′35″W﻿ / ﻿33.3103°N 111.7431°W  \n",
      "87     33°31′59″N 112°11′24″W﻿ / ﻿33.5331°N 112.1899°W  \n",
      "88     36°17′09″N 115°05′38″W﻿ / ﻿36.2857°N 115.0939°W  \n",
      "89       36°06′10″N 80°15′40″W﻿ / ﻿36.1027°N 80.2610°W  \n",
      "90       36°40′46″N 76°18′06″W﻿ / ﻿36.6794°N 76.3018°W  \n",
      "91       36°55′23″N 76°14′41″W﻿ / ﻿36.9230°N 76.2446°W  \n",
      "92     37°29′40″N 121°56′28″W﻿ / ﻿37.4945°N 121.9412°W  \n",
      "93       32°54′35″N 96°37′49″W﻿ / ﻿32.9098°N 96.6303°W  \n",
      "94       32°51′28″N 96°58′12″W﻿ / ﻿32.8577°N 96.9700°W  \n",
      "95       25°52′12″N 80°18′10″W﻿ / ﻿25.8699°N 80.3029°W  \n",
      "96       37°31′53″N 77°28′34″W﻿ / ﻿37.5314°N 77.4760°W  \n",
      "97     43°36′01″N 116°13′54″W﻿ / ﻿43.6002°N 116.2317°W  \n",
      "98     47°40′01″N 117°26′00″W﻿ / ﻿47.6669°N 117.4333°W  \n",
      "99       30°26′32″N 91°07′51″W﻿ / ﻿30.4422°N 91.1309°W  \n",
      "100    47°15′08″N 122°27′35″W﻿ / ﻿47.2522°N 122.4598°W  \n",
      "101    34°08′30″N 117°17′37″W﻿ / ﻿34.1416°N 117.2936°W  \n",
      "102    37°38′15″N 121°00′11″W﻿ / ﻿37.6375°N 121.0030°W  \n",
      "103    34°06′32″N 117°27′46″W﻿ / ﻿34.1090°N 117.4629°W  \n",
      "104      41°34′21″N 93°36′37″W﻿ / ﻿41.5726°N 93.6102°W  \n",
      "105    33°55′24″N 117°12′21″W﻿ / ﻿33.9233°N 117.2057°W  \n",
      "106    34°24′11″N 118°30′15″W﻿ / ﻿34.4030°N 118.5042°W  \n",
      "107      35°04′58″N 78°58′25″W﻿ / ﻿35.0828°N 78.9735°W  \n",
      "108      33°31′39″N 86°47′56″W﻿ / ﻿33.5274°N 86.7990°W  \n",
      "109    34°12′08″N 119°12′17″W﻿ / ﻿34.2023°N 119.2046°W  \n",
      "110      43°10′12″N 77°37′01″W﻿ / ﻿43.1699°N 77.6169°W  \n",
      "111      27°16′50″N 80°23′18″W﻿ / ﻿27.2806°N 80.3883°W  \n",
      "112      42°57′40″N 85°39′20″W﻿ / ﻿42.9612°N 85.6556°W  \n",
      "113      34°41′56″N 86°40′23″W﻿ / ﻿34.6990°N 86.6730°W  \n",
      "114    40°46′37″N 111°55′52″W﻿ / ﻿40.7769°N 111.9310°W  \n",
      "115      33°09′19″N 96°49′21″W﻿ / ﻿33.1554°N 96.8226°W  \n",
      "116      40°56′45″N 73°52′03″W﻿ / ﻿40.9459°N 73.8674°W  \n",
      "117    35°12′00″N 101°49′49″W﻿ / ﻿35.1999°N 101.8302°W  \n",
      "118    34°10′53″N 118°14′45″W﻿ / ﻿34.1814°N 118.2458°W  \n",
      "119    33°41′26″N 118°00′33″W﻿ / ﻿33.6906°N 118.0093°W  \n",
      "120      33°11′55″N 96°40′05″W﻿ / ﻿33.1985°N 96.6680°W  \n",
      "121      32°20′50″N 86°15′58″W﻿ / ﻿32.3472°N 86.2661°W  \n",
      "122      33°21′56″N 82°04′24″W﻿ / ﻿33.3655°N 82.0734°W  \n",
      "123      41°45′49″N 88°17′24″W﻿ / ﻿41.7635°N 88.2901°W  \n",
      "124      41°04′50″N 81°31′17″W﻿ / ﻿41.0805°N 81.5214°W  \n",
      "125      34°43′31″N 92°21′31″W﻿ / ﻿34.7254°N 92.3586°W  \n",
      "126    33°23′18″N 111°55′54″W﻿ / ﻿33.3884°N 111.9318°W  \n",
      "127      32°30′37″N 84°52′30″W﻿ / ﻿32.5102°N 84.8749°W  \n",
      "128      38°53′20″N 94°41′26″W﻿ / ﻿38.8890°N 94.6906°W  \n",
      "129      32°41′13″N 97°01′16″W﻿ / ﻿32.6869°N 97.0211°W  \n",
      "130      30°27′18″N 84°15′12″W﻿ / ﻿30.4551°N 84.2534°W  \n",
      "131      26°38′36″N 81°59′51″W﻿ / ﻿26.6432°N 81.9974°W  \n",
      "132      30°40′06″N 88°06′01″W﻿ / ﻿30.6684°N 88.1002°W  \n",
      "133      35°58′15″N 83°56′57″W﻿ / ﻿35.9707°N 83.9493°W  \n",
      "134      32°28′01″N 93°47′32″W﻿ / ﻿32.4669°N 93.7922°W  \n",
      "135      42°16′10″N 71°48′28″W﻿ / ﻿42.2695°N 71.8078°W  \n",
      "136    34°02′22″N 117°36′15″W﻿ / ﻿34.0394°N 117.6042°W  \n",
      "137    45°38′06″N 122°35′45″W﻿ / ﻿45.6349°N 122.5957°W  \n",
      "138      43°32′18″N 96°43′55″W﻿ / ﻿43.5383°N 96.7320°W  \n",
      "139      35°03′58″N 85°14′54″W﻿ / ﻿35.0660°N 85.2484°W  \n",
      "140      25°59′57″N 97°27′18″W﻿ / ﻿25.9991°N 97.4550°W  \n",
      "141      26°08′28″N 80°08′48″W﻿ / ﻿26.1412°N 80.1467°W  \n",
      "142      41°49′23″N 71°25′08″W﻿ / ﻿41.8231°N 71.4188°W  \n",
      "143      37°04′34″N 76°31′19″W﻿ / ﻿37.0762°N 76.5220°W  \n",
      "144    34°07′24″N 117°33′51″W﻿ / ﻿34.1233°N 117.5642°W  \n",
      "145    38°26′48″N 122°42′22″W﻿ / ﻿38.4468°N 122.7061°W  \n",
      "146    33°47′10″N 112°18′29″W﻿ / ﻿33.7862°N 112.3080°W  \n",
      "147    33°13′28″N 117°18′22″W﻿ / ﻿33.2245°N 117.3062°W  \n",
      "148    38°24′53″N 121°23′06″W﻿ / ﻿38.4146°N 121.3850°W  \n",
      "149    44°55′25″N 123°01′24″W﻿ / ﻿44.9237°N 123.0232°W  \n",
      "150      26°01′16″N 80°20′25″W﻿ / ﻿26.0210°N 80.3404°W  \n",
      "151    44°03′24″N 123°06′58″W﻿ / ﻿44.0567°N 123.1162°W  \n",
      "152    33°46′44″N 117°57′38″W﻿ / ﻿33.7788°N 117.9605°W  \n",
      "153      35°46′51″N 78°48′48″W﻿ / ﻿35.7809°N 78.8133°W  \n",
      "154    40°32′54″N 105°03′53″W﻿ / ﻿40.5482°N 105.0648°W  \n",
      "155    33°51′43″N 117°33′56″W﻿ / ﻿33.8620°N 117.5655°W  \n",
      "156      37°11′39″N 93°17′29″W﻿ / ﻿37.1942°N 93.2913°W  \n",
      "157      32°18′57″N 90°12′46″W﻿ / ﻿32.3158°N 90.2128°W  \n",
      "158      38°49′12″N 77°05′03″W﻿ / ﻿38.8201°N 77.0841°W  \n",
      "159    37°37′43″N 122°06′09″W﻿ / ﻿37.6287°N 122.1024°W  \n",
      "160      36°33′59″N 87°20′43″W﻿ / ﻿36.5664°N 87.3452°W  \n",
      "161    39°41′56″N 105°07′03″W﻿ / ﻿39.6989°N 105.1176°W  \n",
      "162    34°41′37″N 118°10′31″W﻿ / ﻿34.6936°N 118.1753°W  \n",
      "163    36°41′25″N 121°38′01″W﻿ / ﻿36.6902°N 121.6337°W  \n",
      "164    34°35′28″N 118°06′19″W﻿ / ﻿34.5910°N 118.1054°W  \n",
      "165      26°01′52″N 80°09′53″W﻿ / ﻿26.0310°N 80.1646°W  \n",
      "166      42°06′56″N 72°32′24″W﻿ / ﻿42.1155°N 72.5400°W  \n",
      "167      32°48′32″N 83°41′39″W﻿ / ﻿32.8088°N 83.6942°W  \n",
      "168      39°07′21″N 94°44′30″W﻿ / ﻿39.1225°N 94.7418°W  \n",
      "169    37°23′09″N 122°01′35″W﻿ / ﻿37.3858°N 122.0263°W  \n",
      "170    34°03′31″N 117°45′40″W﻿ / ﻿34.0585°N 117.7611°W  \n",
      "171      31°04′40″N 97°43′55″W﻿ / ﻿31.0777°N 97.7320°W  \n",
      "172    33°07′59″N 117°04′26″W﻿ / ﻿33.1331°N 117.0740°W  \n",
      "173      29°39′31″N 95°09′02″W﻿ / ﻿29.6586°N 95.1506°W  \n",
      "174      41°44′57″N 88°09′43″W﻿ / ﻿41.7492°N 88.1620°W  \n",
      "175    47°35′52″N 122°09′23″W﻿ / ﻿47.5979°N 122.1565°W  \n",
      "176      41°31′04″N 88°08′56″W﻿ / ﻿41.5177°N 88.1488°W  \n",
      "177      35°51′08″N 86°24′58″W﻿ / ﻿35.8522°N 86.4160°W  \n",
      "178    32°01′29″N 102°06′49″W﻿ / ﻿32.0246°N 102.1135°W  \n",
      "179      42°15′32″N 89°03′53″W﻿ / ﻿42.2588°N 89.0646°W  \n",
      "180      40°54′53″N 74°09′46″W﻿ / ﻿40.9148°N 74.1628°W  \n",
      "181      32°00′09″N 81°09′13″W﻿ / ﻿32.0025°N 81.1536°W  \n",
      "182      41°11′15″N 73°11′45″W﻿ / ﻿41.1874°N 73.1958°W  \n",
      "183    33°50′06″N 118°20′29″W﻿ / ﻿33.8350°N 118.3414°W  \n",
      "184      26°13′56″N 98°14′47″W﻿ / ﻿26.2322°N 98.2464°W  \n",
      "185      43°02′28″N 76°08′37″W﻿ / ﻿43.0410°N 76.1436°W  \n",
      "186    33°40′14″N 112°27′10″W﻿ / ﻿33.6706°N 112.4527°W  \n",
      "187      33°13′00″N 97°08′29″W﻿ / ﻿33.2166°N 97.1414°W  \n",
      "188    38°46′08″N 121°19′08″W﻿ / ﻿38.7690°N 121.3189°W  \n",
      "189    39°55′10″N 104°56′34″W﻿ / ﻿39.9194°N 104.9428°W  \n",
      "190      25°58′37″N 80°20′09″W﻿ / ﻿25.9770°N 80.3358°W  \n",
      "191    34°09′38″N 118°08′23″W﻿ / ﻿34.1606°N 118.1396°W  \n",
      "192      32°45′46″N 96°35′20″W﻿ / ﻿32.7629°N 96.5888°W  \n",
      "193      38°53′03″N 94°49′10″W﻿ / ﻿38.8843°N 94.8195°W  \n",
      "194      39°46′39″N 84°11′59″W﻿ / ﻿39.7774°N 84.1996°W  \n",
      "195      32°59′18″N 96°53′59″W﻿ / ﻿32.9884°N 96.8998°W  \n",
      "196      31°33′36″N 97°11′10″W﻿ / ﻿31.5601°N 97.1860°W  \n",
      "197    33°47′13″N 117°51′41″W﻿ / ﻿33.7870°N 117.8613°W  \n",
      "198    33°53′09″N 117°55′41″W﻿ / ﻿33.8857°N 117.9280°W  \n",
      "199      32°49′04″N 79°57′32″W﻿ / ﻿32.8179°N 79.9590°W  \n",
      "200    40°41′19″N 112°00′42″W﻿ / ﻿40.6885°N 112.0118°W  \n",
      "201    36°19′38″N 119°19′44″W﻿ / ﻿36.3273°N 119.3289°W  \n",
      "202      37°02′53″N 76°17′50″W﻿ / ﻿37.0480°N 76.2971°W  \n",
      "203      29°40′44″N 82°20′46″W﻿ / ﻿29.6788°N 82.3461°W  \n",
      "204      42°29′34″N 83°01′30″W﻿ / ﻿42.4929°N 83.0250°W  \n",
      "205      26°16′15″N 80°15′33″W﻿ / ﻿26.2707°N 80.2593°W  \n",
      "206      41°58′01″N 91°40′40″W﻿ / ﻿41.9670°N 91.6778°W  \n",
      "207      30°31′31″N 97°39′58″W﻿ / ﻿30.5252°N 97.6660°W  \n",
      "208      42°34′52″N 83°01′49″W﻿ / ﻿42.5812°N 83.0303°W  \n",
      "209    47°23′17″N 122°12′46″W﻿ / ﻿47.3880°N 122.2127°W  \n",
      "210      34°01′45″N 80°53′53″W﻿ / ﻿34.0291°N 80.8980°W  \n",
      "211    37°21′53″N 121°58′04″W﻿ / ﻿37.3646°N 121.9679°W  \n",
      "212      41°18′39″N 72°55′30″W﻿ / ﻿41.3108°N 72.9250°W  \n",
      "213      41°04′48″N 73°32′46″W﻿ / ﻿41.0799°N 73.5460°W  \n",
      "214    37°58′20″N 122°00′06″W﻿ / ﻿37.9722°N 122.0016°W  \n",
      "215      40°39′59″N 74°11′37″W﻿ / ﻿40.6664°N 74.1935°W  \n",
      "216      33°56′59″N 83°22′12″W﻿ / ﻿33.9496°N 83.3701°W  \n",
      "217    34°11′36″N 118°52′27″W﻿ / ﻿34.1933°N 118.8742°W  \n",
      "218      30°12′27″N 92°01′43″W﻿ / ﻿30.2074°N 92.0285°W  \n",
      "219    34°16′01″N 118°44′55″W﻿ / ﻿34.2669°N 118.7485°W  \n",
      "220      39°02′05″N 95°41′46″W﻿ / ﻿39.0347°N 95.6962°W  \n",
      "221      35°14′26″N 97°20′43″W﻿ / ﻿35.2406°N 97.3453°W  \n",
      "222      46°51′55″N 96°49′44″W﻿ / ﻿46.8652°N 96.8290°W  \n",
      "223      34°12′33″N 77°53′09″W﻿ / ﻿34.2092°N 77.8858°W  \n",
      "224      32°27′16″N 99°44′17″W﻿ / ﻿32.4545°N 99.7381°W  \n",
      "225    31°53′02″N 102°20′28″W﻿ / ﻿31.8838°N 102.3411°W  \n",
      "226  38°57′06″N 92°19′43″W﻿ / ﻿38.951561°N 92.328638°W  \n",
      "227      29°33′21″N 95°19′23″W﻿ / ﻿29.5558°N 95.3231°W  \n",
      "228    34°31′40″N 117°21′13″W﻿ / ﻿34.5277°N 117.3536°W  \n",
      "229      41°45′57″N 72°40′54″W﻿ / ﻿41.7659°N 72.6816°W  \n",
      "230    38°06′28″N 122°15′50″W﻿ / ﻿38.1079°N 122.2640°W  \n",
      "231      40°35′37″N 75°28′42″W﻿ / ﻿40.5936°N 75.4784°W  \n",
      "232    37°52′01″N 122°17′57″W﻿ / ﻿37.8670°N 122.2991°W  \n",
      "233      32°58′20″N 96°42′29″W﻿ / ﻿32.9723°N 96.7081°W  \n",
      "234    39°50′01″N 105°09′01″W﻿ / ﻿39.8337°N 105.1503°W  \n",
      "235      42°16′34″N 83°43′51″W﻿ / ﻿42.2761°N 83.7309°W  \n",
      "236      44°00′55″N 92°28′38″W﻿ / ﻿44.0154°N 92.4772°W  \n",
      "237      42°22′34″N 71°07′07″W﻿ / ﻿42.3760°N 71.1187°W  \n",
      "238      29°35′58″N 95°36′51″W﻿ / ﻿29.5994°N 95.6142°W  \n",
      "239      42°42′51″N 84°33′33″W﻿ / ﻿42.7143°N 84.5593°W  \n",
      "240      37°59′16″N 87°32′05″W﻿ / ﻿37.9877°N 87.5347°W  \n",
      "241      30°35′07″N 96°17′47″W﻿ / ﻿30.5852°N 96.2964°W  \n",
      "242    38°15′33″N 122°01′56″W﻿ / ﻿38.2593°N 122.0321°W  \n",
      "243      27°58′44″N 82°46′00″W﻿ / ﻿27.9789°N 82.7666°W  \n",
      "244      30°05′06″N 94°08′43″W﻿ / ﻿30.0849°N 94.1453°W  \n",
      "245      39°05′08″N 94°21′08″W﻿ / ﻿39.0855°N 94.3521°W  \n",
      "246    40°14′43″N 111°38′41″W﻿ / ﻿40.2453°N 111.6448°W  \n",
      "247    40°36′09″N 112°00′03″W﻿ / ﻿40.6024°N 112.0008°W  \n",
      "248    33°34′20″N 117°11′25″W﻿ / ﻿33.5721°N 117.1904°W  \n",
      "249      27°59′08″N 80°39′45″W﻿ / ﻿27.9856°N 80.6626°W  \n",
      "250    34°04′29″N 118°01′45″W﻿ / ﻿34.0746°N 118.0291°W  \n",
      "251    33°07′26″N 117°16′58″W﻿ / ﻿33.1239°N 117.2828°W  \n",
      "252      32°55′04″N 80°03′54″W﻿ / ﻿32.9178°N 80.0650°W  \n",
      "253    33°29′35″N 117°07′54″W﻿ / ﻿33.4931°N 117.1317°W  \n",
      "254    36°49′42″N 119°41′06″W﻿ / ﻿36.8282°N 119.6849°W  \n",
      "255      39°47′28″N 89°38′41″W﻿ / ﻿39.7911°N 89.6446°W  \n",
      "256    43°36′51″N 116°23′56″W﻿ / ﻿43.6142°N 116.3989°W  \n",
      "257    39°52′56″N 105°03′52″W﻿ / ﻿39.8822°N 105.0644°W  \n",
      "258    33°39′57″N 117°54′44″W﻿ / ﻿33.6659°N 117.9123°W  \n",
      "259      35°59′24″N 79°59′26″W﻿ / ﻿35.9900°N 79.9905°W  \n",
      "260      42°59′06″N 71°26′39″W﻿ / ﻿42.9849°N 71.4441°W  \n",
      "261    38°16′12″N 104°36′44″W﻿ / ﻿38.2699°N 104.6123°W  \n",
      "262      28°03′20″N 81°57′18″W﻿ / ﻿28.0555°N 81.9549°W  \n",
      "263      26°14′30″N 80°08′02″W﻿ / ﻿26.2416°N 80.1339°W  \n",
      "264      26°44′47″N 80°07′30″W﻿ / ﻿26.7464°N 80.1251°W  \n",
      "265    37°58′45″N 121°47′46″W﻿ / ﻿37.9791°N 121.7962°W  \n",
      "266    47°57′24″N 122°11′29″W﻿ / ﻿47.9566°N 122.1914°W  \n",
      "267    33°56′18″N 118°07′51″W﻿ / ﻿33.9382°N 118.1309°W  \n",
      "268      42°38′20″N 71°19′16″W﻿ / ﻿42.6390°N 71.3211°W  \n",
      "269    39°35′26″N 104°52′09″W﻿ / ﻿39.5906°N 104.8691°W  \n",
      "270      42°02′23″N 88°19′18″W﻿ / ﻿42.0396°N 88.3217°W  \n",
      "271    37°57′08″N 122°21′38″W﻿ / ﻿37.9523°N 122.3606°W  \n",
      "272      40°45′05″N 89°37′03″W﻿ / ﻿40.7515°N 89.6174°W  \n",
      "273      36°02′11″N 95°46′52″W﻿ / ﻿36.0365°N 95.7810°W  \n",
      "274      25°56′56″N 80°14′37″W﻿ / ﻿25.9489°N 80.2436°W  \n",
      "275    45°47′19″N 108°33′00″W﻿ / ﻿45.7885°N 108.5499°W  \n",
      "276    34°00′09″N 117°28′03″W﻿ / ﻿34.0026°N 117.4676°W  \n",
      "277      33°55′53″N 84°22′07″W﻿ / ﻿33.9315°N 84.3687°W  \n",
      "278    45°30′08″N 122°26′30″W﻿ / ﻿45.5023°N 122.4416°W  \n",
      "279      33°02′48″N 96°58′54″W﻿ / ﻿33.0466°N 96.9818°W  \n",
      "280    45°31′41″N 122°56′09″W﻿ / ﻿45.5280°N 122.9357°W  \n",
      "281    34°16′04″N 119°15′15″W﻿ / ﻿34.2678°N 119.2542°W  \n",
      "282    40°24′55″N 104°46′11″W﻿ / ﻿40.4153°N 104.7697°W  \n",
      "283    33°57′22″N 118°20′39″W﻿ / ﻿33.9561°N 118.3443°W  \n",
      "284      41°33′31″N 73°02′12″W﻿ / ﻿41.5585°N 73.0367°W  \n",
      "285      29°29′24″N 95°06′33″W﻿ / ﻿29.4901°N 95.1091°W  \n",
      "286    34°56′00″N 120°26′38″W﻿ / ﻿34.9332°N 120.4438°W  \n",
      "287      32°19′02″N 95°18′21″W﻿ / ﻿32.3173°N 95.3059°W  \n",
      "288      26°04′45″N 80°17′06″W﻿ / ﻿26.0791°N 80.2850°W  \n",
      "289      40°04′38″N 74°12′01″W﻿ / ﻿40.0771°N 74.2004°W  \n",
      "290    37°42′03″N 122°27′54″W﻿ / ﻿37.7009°N 122.4650°W  \n",
      "291    40°01′37″N 105°15′07″W﻿ / ﻿40.0270°N 105.2519°W  \n",
      "292      33°05′59″N 96°39′47″W﻿ / ﻿33.0997°N 96.6631°W  \n",
      "293    34°03′21″N 117°54′36″W﻿ / ﻿34.0559°N 117.9099°W  \n",
      "294    39°33′16″N 119°44′08″W﻿ / ﻿39.5544°N 119.7356°W  \n",
      "295      33°54′24″N 98°31′33″W﻿ / ﻿33.9067°N 98.5259°W  \n",
      "296      44°31′15″N 87°59′03″W﻿ / ﻿44.5207°N 87.9842°W  \n",
      "297    37°33′37″N 122°18′38″W﻿ / ﻿37.5603°N 122.3106°W  \n",
      "298    33°54′27″N 118°05′01″W﻿ / ﻿33.9076°N 118.0835°W  \n",
      "299    34°06′42″N 117°23′18″W﻿ / ﻿34.1118°N 117.3883°W  \n",
      "300    32°19′35″N 106°47′23″W﻿ / ﻿32.3264°N 106.7897°W  \n",
      "301    39°44′24″N 121°50′08″W﻿ / ﻿39.7400°N 121.8356°W  \n",
      "302    32°48′06″N 116°57′37″W﻿ / ﻿32.8017°N 116.9604°W  \n",
      "303    34°11′24″N 118°19′35″W﻿ / ﻿34.1901°N 118.3264°W  \n",
      "304      41°40′37″N 86°16′08″W﻿ / ﻿41.6769°N 86.2690°W  \n",
      "305    47°28′34″N 122°11′31″W﻿ / ﻿47.4761°N 122.1920°W  \n",
      "306    33°11′22″N 117°14′19″W﻿ / ﻿33.1895°N 117.2386°W  \n",
      "307      41°33′15″N 90°36′14″W﻿ / ﻿41.5541°N 90.6040°W  \n",
      "308      26°18′15″N 98°09′50″W﻿ / ﻿26.3042°N 98.1639°W  \n",
      "309      33°12′23″N 87°32′05″W﻿ / ﻿33.2065°N 87.5346°W  \n",
      "310      39°58′00″N 86°06′00″W﻿ / ﻿39.9667°N 86.1000°W  \n",
      "311    47°40′24″N 117°14′22″W﻿ / ﻿47.6733°N 117.2394°W  \n",
      "312    31°26′28″N 100°27′02″W﻿ / ﻿31.4411°N 100.4505°W  \n",
      "313    38°21′14″N 121°58′22″W﻿ / ﻿38.3539°N 121.9728°W  \n",
      "314      42°35′25″N 82°55′01″W﻿ / ﻿42.5903°N 82.9170°W  \n",
      "315    44°03′00″N 121°18′00″W﻿ / ﻿44.0500°N 121.3000°W  \n",
      "316      40°33′39″N 74°17′34″W﻿ / ﻿40.5607°N 74.2927°W  \n"
     ]
    }
   ],
   "source": [
    "# Code to retrieve the cities\n",
    "\n",
    "url = 'https://en.m.wikipedia.org/wiki/List_of_United_States_cities_by_population'\n",
    "dfs = pd.read_html(url)\n",
    "\n",
    "print(len(dfs))\n",
    "\n",
    "print(dfs[3])\n",
    "\n",
    "df = dfs[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will clean the city names to make them standard and usable in our queries to Foursquare API. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019rank</th>\n",
       "      <th>City</th>\n",
       "      <th>State[c]</th>\n",
       "      <th>2019estimate</th>\n",
       "      <th>2010Census</th>\n",
       "      <th>Change</th>\n",
       "      <th>2016 land area</th>\n",
       "      <th>2016 land area.1</th>\n",
       "      <th>2016 population density</th>\n",
       "      <th>2016 population density.1</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>8336817</td>\n",
       "      <td>8175133</td>\n",
       "      <td>+1.98%</td>\n",
       "      <td>301.5 sq mi</td>\n",
       "      <td>780.9 km2</td>\n",
       "      <td>28,317/sq mi</td>\n",
       "      <td>10933</td>\n",
       "      <td>40°39′49″N 73°56′19″W﻿ / ﻿40.6635°N 73.9387°W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>3979576</td>\n",
       "      <td>3792621</td>\n",
       "      <td>+4.93%</td>\n",
       "      <td>468.7 sq mi</td>\n",
       "      <td>1,213.9 km2</td>\n",
       "      <td>8,484/sq mi</td>\n",
       "      <td>3276</td>\n",
       "      <td>34°01′10″N 118°24′39″W﻿ / ﻿34.0194°N 118.4108°W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>2693976</td>\n",
       "      <td>2695598</td>\n",
       "      <td>−0.06%</td>\n",
       "      <td>227.3 sq mi</td>\n",
       "      <td>588.7 km2</td>\n",
       "      <td>11,900/sq mi</td>\n",
       "      <td>4600</td>\n",
       "      <td>41°50′15″N 87°40′54″W﻿ / ﻿41.8376°N 87.6818°W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>2320268</td>\n",
       "      <td>2100263</td>\n",
       "      <td>+10.48%</td>\n",
       "      <td>637.5 sq mi</td>\n",
       "      <td>1,651.1 km2</td>\n",
       "      <td>3,613/sq mi</td>\n",
       "      <td>1395</td>\n",
       "      <td>29°47′12″N 95°23′27″W﻿ / ﻿29.7866°N 95.3909°W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1680992</td>\n",
       "      <td>1445632</td>\n",
       "      <td>+16.28%</td>\n",
       "      <td>517.6 sq mi</td>\n",
       "      <td>1,340.6 km2</td>\n",
       "      <td>3,120/sq mi</td>\n",
       "      <td>1200</td>\n",
       "      <td>33°34′20″N 112°05′24″W﻿ / ﻿33.5722°N 112.0901°W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2019rank           City    State[c]  2019estimate  2010Census   Change  \\\n",
       "0         1  New York City    New York       8336817     8175133   +1.98%   \n",
       "1         2    Los Angeles  California       3979576     3792621   +4.93%   \n",
       "2         3        Chicago    Illinois       2693976     2695598   −0.06%   \n",
       "3         4        Houston       Texas       2320268     2100263  +10.48%   \n",
       "4         5        Phoenix     Arizona       1680992     1445632  +16.28%   \n",
       "\n",
       "  2016 land area 2016 land area.1 2016 population density  \\\n",
       "0    301.5 sq mi        780.9 km2            28,317/sq mi   \n",
       "1    468.7 sq mi      1,213.9 km2             8,484/sq mi   \n",
       "2    227.3 sq mi        588.7 km2            11,900/sq mi   \n",
       "3    637.5 sq mi      1,651.1 km2             3,613/sq mi   \n",
       "4    517.6 sq mi      1,340.6 km2             3,120/sq mi   \n",
       "\n",
       "  2016 population density.1                                         Location  \n",
       "0                     10933    40°39′49″N 73°56′19″W﻿ / ﻿40.6635°N 73.9387°W  \n",
       "1                      3276  34°01′10″N 118°24′39″W﻿ / ﻿34.0194°N 118.4108°W  \n",
       "2                      4600    41°50′15″N 87°40′54″W﻿ / ﻿41.8376°N 87.6818°W  \n",
       "3                      1395    29°47′12″N 95°23′27″W﻿ / ﻿29.7866°N 95.3909°W  \n",
       "4                      1200  33°34′20″N 112°05′24″W﻿ / ﻿33.5722°N 112.0901°W  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['City'] = df['City'].str.replace(\"\\[.*\\]\",\"\")\n",
    "df['2016 population density.1'] = df['2016 population density.1'].str.replace(\"/km2\",\"\")\n",
    "df['2016 population density.1'] = df['2016 population density.1'].str.replace(\",\",\"\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-40-2d1a09afe1e8>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-40-2d1a09afe1e8>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    df = df[[\"City\",\"State[c]\",\"2019estimate\",\"2016 population density.1\":\"Population density\"]]\u001b[0m\n\u001b[0m                                                                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "df = df[[\"City\",\"State[c]\",\"2019estimate\",\"2016 population density.1\":\"Population density\"]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019rank</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>2010Census</th>\n",
       "      <th>Change</th>\n",
       "      <th>2016 land area</th>\n",
       "      <th>2016 land area.1</th>\n",
       "      <th>2016 population density</th>\n",
       "      <th>Population density</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8175133</td>\n",
       "      <td>+1.98%</td>\n",
       "      <td>301.5 sq mi</td>\n",
       "      <td>780.9 km2</td>\n",
       "      <td>28,317/sq mi</td>\n",
       "      <td>10933</td>\n",
       "      <td>40°39′49″N 73°56′19″W﻿ / ﻿40.6635°N 73.9387°W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3792621</td>\n",
       "      <td>+4.93%</td>\n",
       "      <td>468.7 sq mi</td>\n",
       "      <td>1,213.9 km2</td>\n",
       "      <td>8,484/sq mi</td>\n",
       "      <td>3276</td>\n",
       "      <td>34°01′10″N 118°24′39″W﻿ / ﻿34.0194°N 118.4108°W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2695598</td>\n",
       "      <td>−0.06%</td>\n",
       "      <td>227.3 sq mi</td>\n",
       "      <td>588.7 km2</td>\n",
       "      <td>11,900/sq mi</td>\n",
       "      <td>4600</td>\n",
       "      <td>41°50′15″N 87°40′54″W﻿ / ﻿41.8376°N 87.6818°W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2100263</td>\n",
       "      <td>+10.48%</td>\n",
       "      <td>637.5 sq mi</td>\n",
       "      <td>1,651.1 km2</td>\n",
       "      <td>3,613/sq mi</td>\n",
       "      <td>1395</td>\n",
       "      <td>29°47′12″N 95°23′27″W﻿ / ﻿29.7866°N 95.3909°W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1445632</td>\n",
       "      <td>+16.28%</td>\n",
       "      <td>517.6 sq mi</td>\n",
       "      <td>1,340.6 km2</td>\n",
       "      <td>3,120/sq mi</td>\n",
       "      <td>1200</td>\n",
       "      <td>33°34′20″N 112°05′24″W﻿ / ﻿33.5722°N 112.0901°W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2019rank           City       State  Population  2010Census   Change  \\\n",
       "0         1  New York City    New York         8.3     8175133   +1.98%   \n",
       "1         2    Los Angeles  California         4.0     3792621   +4.93%   \n",
       "2         3        Chicago    Illinois         2.7     2695598   −0.06%   \n",
       "3         4        Houston       Texas         2.3     2100263  +10.48%   \n",
       "4         5        Phoenix     Arizona         1.7     1445632  +16.28%   \n",
       "\n",
       "  2016 land area 2016 land area.1 2016 population density Population density  \\\n",
       "0    301.5 sq mi        780.9 km2            28,317/sq mi              10933   \n",
       "1    468.7 sq mi      1,213.9 km2             8,484/sq mi               3276   \n",
       "2    227.3 sq mi        588.7 km2            11,900/sq mi               4600   \n",
       "3    637.5 sq mi      1,651.1 km2             3,613/sq mi               1395   \n",
       "4    517.6 sq mi      1,340.6 km2             3,120/sq mi               1200   \n",
       "\n",
       "                                          Location  \n",
       "0    40°39′49″N 73°56′19″W﻿ / ﻿40.6635°N 73.9387°W  \n",
       "1  34°01′10″N 118°24′39″W﻿ / ﻿34.0194°N 118.4108°W  \n",
       "2    41°50′15″N 87°40′54″W﻿ / ﻿41.8376°N 87.6818°W  \n",
       "3    29°47′12″N 95°23′27″W﻿ / ﻿29.7866°N 95.3909°W  \n",
       "4  33°34′20″N 112°05′24″W﻿ / ﻿33.5722°N 112.0901°W  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={\"State[c]\":\"State\",\"2019estimate\":\"Population\",\"2016 population density.1\":\"Population density\"},inplace = True)\n",
    "df = df.assign(Population = round(df[\"Population\"]/1000000,1))\n",
    "# df[\"Population\"] = round(df[\"Population\"]/1000000,1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['New York City', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia', 'San Antonio', 'San Diego', 'Dallas', 'San Jose', 'Austin', 'Jacksonville', 'Fort Worth', 'Columbus', 'Charlotte', 'San Francisco', 'Indianapolis', 'Seattle', 'Denver', 'Washington', 'Boston', 'El Paso', 'Nashville', 'Detroit', 'Oklahoma City', 'Portland', 'Las Vegas', 'Memphis', 'Louisville', 'Baltimore', 'Milwaukee', 'Albuquerque', 'Tucson', 'Fresno', 'Mesa', 'Sacramento', 'Atlanta', 'Kansas City', 'Colorado Springs', 'Omaha', 'Raleigh', 'Miami', 'Long Beach', 'Virginia Beach', 'Oakland', 'Minneapolis', 'Tulsa', 'Tampa', 'Arlington', 'New Orleans', 'Wichita', 'Bakersfield', 'Cleveland', 'Aurora', 'Anaheim', 'Honolulu', 'Santa Ana', 'Riverside', 'Corpus Christi', 'Lexington', 'Henderson', 'Stockton', 'Saint Paul', 'Cincinnati', 'St. Louis', 'Pittsburgh', 'Greensboro', 'Lincoln', 'Anchorage', 'Plano', 'Orlando', 'Irvine', 'Newark', 'Durham', 'Chula Vista', 'Toledo', 'Fort Wayne', 'St. Petersburg', 'Laredo', 'Jersey City', 'Chandler', 'Madison', 'Lubbock', 'Scottsdale', 'Reno', 'Buffalo', 'Gilbert', 'Glendale', 'North Las Vegas', 'Winston–Salem', 'Chesapeake', 'Norfolk', 'Fremont', 'Garland', 'Irving', 'Hialeah', 'Richmond', 'Boise', 'Spokane', 'Baton Rouge']\n"
     ]
    }
   ],
   "source": [
    "# The scope of the analysis will be the top 100 cities ranked by population\n",
    "cities = list(df['City'][0:100])\n",
    "print(cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Retrieving venue data from Foursquare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Foursquare API credentials and endpoint\n",
    "CLIENT_ID = os.getenv(\"CLIENT_ID\")\n",
    "CLIENT_SECRET = os.getenv(\"CLIENT_SECRET\")\n",
    "VERSION = '20200616'\n",
    "URL = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&limit={}'.format(CLIENT_ID, CLIENT_SECRET, VERSION, 5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now load the types of cuisines available in Foursquare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cuisines = {\n",
    "    'Afghan': '503288ae91d4c4b30a586d67', \n",
    "    'African': '4bf58dd8d48988d1c8941735', \n",
    "    'American': '4bf58dd8d48988d14e941735', \n",
    "    'Burmese': '56aa371be4b08b9a8d573568', \n",
    "    'Cambodian': '52e81612bcbc57f1066b7a03', \n",
    "    'Chinese': '4bf58dd8d48988d145941735', \n",
    "    'Filipino': '4eb1bd1c3b7b55596b4a748f', \n",
    "    'Himalayan': '52e81612bcbc57f1066b79fb', \n",
    "    'Indonesian': '4deefc054765f83613cdba6f', \n",
    "    'Japanese': '4bf58dd8d48988d111941735', \n",
    "    'Korean': '4bf58dd8d48988d113941735', \n",
    "    'Malay': '4bf58dd8d48988d156941735', \n",
    "    'Mongolian': '4eb1d5724b900d56c88a45fe', \n",
    "    'Thai': '4bf58dd8d48988d149941735', \n",
    "    'Tibetan': '52af39fb3cf9994f4e043be9', \n",
    "    'Vietnamese': '4bf58dd8d48988d14a941735', \n",
    "    'Australian': '4bf58dd8d48988d169941735', \n",
    "    'Austrian': '52e81612bcbc57f1066b7a01', \n",
    "    'Bangladeshi': '5e179ee74ae8e90006e9a746', \n",
    "    'Belgian': '52e81612bcbc57f1066b7a02', \n",
    "    'Caribbean': '4bf58dd8d48988d144941735', \n",
    "    'Caucasian': '5293a7d53cf9994f4e043a45', \n",
    "    'Czech': '52f2ae52bcbc57f1066b8b81', \n",
    "    'Dutch': '5744ccdfe4b0c0459246b4d0', \n",
    "    'Belarusian': '52e928d0bcbc57f1066b7e97', \n",
    "    'Bosnian': '58daa1558bbb0b01f18ec1ee', \n",
    "    'Bulgarian': '56aa371be4b08b9a8d5734f3', \n",
    "    'Romanian': '52960bac3cf9994f4e043ac4', \n",
    "    'Tatar': '52e928d0bcbc57f1066b7e98', \n",
    "    'English': '52e81612bcbc57f1066b7a05', \n",
    "    'French': '4bf58dd8d48988d10c941735', \n",
    "    'German': '4bf58dd8d48988d10d941735', \n",
    "    'Greek': '4bf58dd8d48988d10e941735', \n",
    "    'Hawaiian': '52e81612bcbc57f1066b79fe', \n",
    "    'Hungarian': '52e81612bcbc57f1066b79fa', \n",
    "    'Indian': '4bf58dd8d48988d10f941735', \n",
    "    'Italian': '4bf58dd8d48988d110941735', \n",
    "    'Salvadoran': '5745c7ac498e5d0483112fdb', \n",
    "    'Argentinian': '4bf58dd8d48988d107941735', \n",
    "    'Brazilian': '4bf58dd8d48988d16b941735', \n",
    "    'Colombian': '58daa1558bbb0b01f18ec1f4', \n",
    "    'Peruvian': '4eb1bfa43b7b52c0e1adc2e8', \n",
    "    'Venezuelan': '56aa371be4b08b9a8d573558', \n",
    "    'Moroccan': '4bf58dd8d48988d1c3941735', \n",
    "    'Mexican': '4bf58dd8d48988d1c1941735', \n",
    "    'Egyptian': '5bae9231bedf3950379f89e1', \n",
    "    'Iraqi': '5bae9231bedf3950379f89e7', \n",
    "    'Israeli': '56aa371be4b08b9a8d573529', \n",
    "    'Kurdish': '5744ccdfe4b0c0459246b4ca', \n",
    "    'Lebanese': '58daa1558bbb0b01f18ec1cd', \n",
    "    'Persian': '52e81612bcbc57f1066b79f7', \n",
    "    'Syrian': '5bae9231bedf3950379f89da', \n",
    "    'Yemeni': '5bae9231bedf3950379f89ea', \n",
    "    'Pakistani': '52e81612bcbc57f1066b79f8', \n",
    "    'Polish': '52e81612bcbc57f1066b7a04', \n",
    "    'Portuguese': '4def73e84765ae376e57713a', \n",
    "    'Russian': '5293a7563cf9994f4e043a44', \n",
    "    'Scandinavian': '4bf58dd8d48988d1c6941735', \n",
    "    'Scottish': '5744ccdde4b0c0459246b4a3', \n",
    "    'Slovak': '56aa371be4b08b9a8d57355a', \n",
    "    'Spanish': '4bf58dd8d48988d150941735', \n",
    "    'Sri Lankan': '5413605de4b0ae91d18581a9', \n",
    "    'Swiss': '4bf58dd8d48988d158941735', \n",
    "    'Turkish': '4f04af1f2fb6e1c99f3db0bb', \n",
    "    'Ukranian': '52e928d0bcbc57f1066b7e96'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We continue with the code to retrieve all the restaurants for each city and cuisine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Newark\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-71-f70c9151ea25>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcuisine_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcategory_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcuisines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0;31m# For each city, retrieve the top restaurants from Foursquare for each cuisine...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m             \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{url}&near={city}&categoryId={category_id}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mURL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcategory_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategory_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m                 \u001b[0mnumber\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    528\u001b[0m         }\n\u001b[1;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m             \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m             httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m    671\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    424\u001b[0m                     \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m                     \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 426\u001b[0;31m                     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    427\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    419\u001b[0m                 \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1330\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1331\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1332\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1333\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 303\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    304\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    667\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    670\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1239\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1240\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1241\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1242\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1243\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1097\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1098\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1100\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\"\"\"Extract nested values from a JSON tree.\"\"\"\n",
    "\n",
    "\n",
    "def json_extract(obj, key):\n",
    "    \"\"\"Recursively fetch values from nested JSON.\"\"\"\n",
    "    arr = []\n",
    "\n",
    "    def extract(obj, arr, key):\n",
    "        \"\"\"Recursively search for values of key in JSON tree.\"\"\"\n",
    "        if isinstance(obj, dict):\n",
    "            for k, v in obj.items():\n",
    "                if isinstance(v, (dict, list)):\n",
    "                    extract(v, arr, key)\n",
    "                elif k == key:\n",
    "                    arr.append(v)\n",
    "        elif isinstance(obj, list):\n",
    "            for item in obj:\n",
    "                extract(item, arr, key)\n",
    "        return arr\n",
    "\n",
    "    values = extract(obj, arr, key)\n",
    "    return values\n",
    "\n",
    "\n",
    "# Important: In order to avoid loading the data from the Foursquare API every time the notebook is reloaded, we can download the data to a .csv file once and then reuse it. \n",
    "# Write DataFrame to CSV, use the first time you want to download the DataFrame to a CSV\n",
    "\n",
    "\n",
    "read_local_file = False\n",
    "#'Newark','Durham'\n",
    "cities = ['Newark']\n",
    "\n",
    "if (read_local_file == True):\n",
    "    restaurants_from_csv = pd.read_csv('restaurants.csv', keep_default_na=False, na_values=[\"\"])\n",
    "    restaurants = restaurants_from_csv\n",
    "else:\n",
    "    restaurants = pd.DataFrame(columns=['City', 'Cuisine', 'Total restaurants'])\n",
    "    for city in cities:\n",
    "        print(city)\n",
    "        for cuisine_name, category_id in cuisines.items():\n",
    "            # For each city, retrieve the top restaurants from Foursquare for each cuisine...\n",
    "            query = requests.get(\"{url}&near={city}&categoryId={category_id}\".format(url=URL, city=city, category_id=category_id))\n",
    "            if query.status_code == 200:\n",
    "                number = query.json()\n",
    "                #print(json.dumps(number, indent=4, sort_keys=True))\n",
    "                n = 0\n",
    "                id_list = list()\n",
    "                \n",
    "                try:     \n",
    "                    #id_list = (json.dumps(number['response']['groups'][0]['items'][0]['venue']['id'], indent=4, sort_keys=True))\n",
    "                    \n",
    "                    id = number['response']['groups'][0]['items'][0]['venue']['id']\n",
    "                    \n",
    "                except IndexError:\n",
    "                    pass\n",
    "                continue\n",
    "                \n",
    "                print (id_list)\n",
    "                #for (k, v) in number.items():\n",
    "                #   print(\"Key: \" + k)\n",
    "                #   print(\"Value: \" + str(v))\n",
    "                #restaurants = restaurants.append({\n",
    "                #    'City': city,\n",
    "                #    'Cuisine': cuisine_name,\n",
    "                #    'Total restaurants': number['response']['totalResults']\n",
    "                #}, ignore_index=True)\n",
    "        \n",
    "#restaurants\n",
    "#restaurants.to_csv('restaurants_total.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see what cities have been properly downloaded\n",
    "len(np.unique(restaurants['City']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some reason, after several attemps, Foursquare API does only respond with data for 14 cities. It looks like there is a limitation from Foursquare. Let's continue with it.    \n",
    "We will calculate the total restaurants per city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Diversity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>New York City</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Jersey City</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Portland</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Newark</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Boston</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Miami</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Washington</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Austin</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Denver</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Houston</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Detroit</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Irving</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Long Beach</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Plano</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>New Orleans</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Orlando</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>San Antonio</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Raleigh</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Santa Ana</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Garland</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Hialeah</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Mesa</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Nashville</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Charlotte</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Madison</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Irvine</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Tampa</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Gilbert</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Honolulu</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Durham</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>North Las Vegas</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Tulsa</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Virginia Beach</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Norfolk</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Omaha</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Boise</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Greensboro</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Reno</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Chesapeake</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Lexington</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Wichita</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Stockton</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Toledo</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>El Paso</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Laredo</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  Diversity\n",
       "51           Memphis       65.0\n",
       "57     New York City       61.0\n",
       "41       Jersey City       60.0\n",
       "14           Chicago       57.0\n",
       "78     San Francisco       54.0\n",
       "48       Los Angeles       53.0\n",
       "65      Philadelphia       52.0\n",
       "69          Portland       52.0\n",
       "61           Oakland       52.0\n",
       "85    St. Petersburg       52.0\n",
       "58            Newark       52.0\n",
       "9             Boston       52.0\n",
       "82           Seattle       51.0\n",
       "21            Dallas       51.0\n",
       "53             Miami       50.0\n",
       "91        Washington       50.0\n",
       "5             Austin       50.0\n",
       "44         Las Vegas       49.0\n",
       "22            Denver       49.0\n",
       "54       Minneapolis       48.0\n",
       "36           Houston       48.0\n",
       "0            Anaheim       48.0\n",
       "23           Detroit       47.0\n",
       "19          Columbus       47.0\n",
       "4             Aurora       47.0\n",
       "75        Saint Paul       47.0\n",
       "39            Irving       46.0\n",
       "2          Arlington       46.0\n",
       "79          San Jose       46.0\n",
       "47        Long Beach       46.0\n",
       "77         San Diego       45.0\n",
       "81        Scottsdale       45.0\n",
       "68             Plano       45.0\n",
       "56       New Orleans       45.0\n",
       "67        Pittsburgh       45.0\n",
       "17         Cleveland       45.0\n",
       "66           Phoenix       45.0\n",
       "64           Orlando       45.0\n",
       "76       San Antonio       44.0\n",
       "70           Raleigh       44.0\n",
       "80         Santa Ana       44.0\n",
       "3            Atlanta       44.0\n",
       "28           Fremont       44.0\n",
       "29           Garland       44.0\n",
       "34           Hialeah       43.0\n",
       "42       Kansas City       43.0\n",
       "52              Mesa       43.0\n",
       "55         Nashville       42.0\n",
       "84         St. Louis       42.0\n",
       "74        Sacramento       42.0\n",
       "12         Charlotte       42.0\n",
       "15       Chula Vista       42.0\n",
       "50           Madison       42.0\n",
       "38            Irvine       41.0\n",
       "11          Chandler       41.0\n",
       "87             Tampa       41.0\n",
       "30           Gilbert       41.0\n",
       "37      Indianapolis       41.0\n",
       "33         Henderson       41.0\n",
       "35          Honolulu       40.0\n",
       "24            Durham       40.0\n",
       "60   North Las Vegas       39.0\n",
       "16        Cincinnati       38.0\n",
       "27        Fort Worth       38.0\n",
       "73         Riverside       37.0\n",
       "31          Glendale       37.0\n",
       "40      Jacksonville       37.0\n",
       "18  Colorado Springs       36.0\n",
       "10           Buffalo       36.0\n",
       "62     Oklahoma City       35.0\n",
       "89             Tulsa       34.0\n",
       "72          Richmond       34.0\n",
       "90    Virginia Beach       34.0\n",
       "59           Norfolk       34.0\n",
       "63             Omaha       33.0\n",
       "8              Boise       32.0\n",
       "32        Greensboro       32.0\n",
       "71              Reno       31.0\n",
       "13        Chesapeake       30.0\n",
       "83           Spokane       30.0\n",
       "45         Lexington       30.0\n",
       "92           Wichita       29.0\n",
       "86          Stockton       28.0\n",
       "20    Corpus Christi       28.0\n",
       "88            Toledo       27.0\n",
       "1          Anchorage       26.0\n",
       "46           Lincoln       26.0\n",
       "26        Fort Wayne       25.0\n",
       "25           El Paso       25.0\n",
       "7        Baton Rouge       25.0\n",
       "6        Bakersfield       25.0\n",
       "49           Lubbock       22.0\n",
       "43            Laredo       16.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the diversity of cuisines per city\n",
    "restaurants['Diversity'] = restaurants['Total restaurants']>0\n",
    "restaurants.head(100)\n",
    "\n",
    "restaurants[['City','Diversity']].groupby('City').sum().reset_index().sort_values(by = 'Diversity', ascending = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>Total restaurants</th>\n",
       "      <th>Diversity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York City</td>\n",
       "      <td>Afghan</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York City</td>\n",
       "      <td>African</td>\n",
       "      <td>91</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York City</td>\n",
       "      <td>American</td>\n",
       "      <td>375</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York City</td>\n",
       "      <td>Burmese</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York City</td>\n",
       "      <td>Cambodian</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City    Cuisine  Total restaurants  Diversity\n",
       "0  New York City     Afghan                 13       True\n",
       "1  New York City    African                 91       True\n",
       "2  New York City   American                375       True\n",
       "3  New York City    Burmese                  9       True\n",
       "4  New York City  Cambodian                  9       True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Total restaurants</th>\n",
       "      <th>Diversity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>1451</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>413</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>1114</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>1940</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>1011</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City  Total restaurants  Diversity\n",
       "0    Anaheim               1451       48.0\n",
       "1  Anchorage                413       26.0\n",
       "2  Arlington               1114       46.0\n",
       "3    Atlanta               1940       44.0\n",
       "4     Aurora               1011       47.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants_total = restaurants.groupby('City').sum().reset_index()\n",
    "restaurants_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Total restaurants</th>\n",
       "      <th>Diversity</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>New York City</td>\n",
       "      <td>5493</td>\n",
       "      <td>61.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3929</td>\n",
       "      <td>53.0</td>\n",
       "      <td>California</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>3210</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Houston</td>\n",
       "      <td>2959</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>1612</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>2457</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>San Antonio</td>\n",
       "      <td>1596</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>2350</td>\n",
       "      <td>45.0</td>\n",
       "      <td>California</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>2216</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Austin</td>\n",
       "      <td>1640</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1732</td>\n",
       "      <td>46.0</td>\n",
       "      <td>California</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>1206</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>1002</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>1285</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>1236</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Charlotte</td>\n",
       "      <td>1584</td>\n",
       "      <td>42.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>3117</td>\n",
       "      <td>54.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2334</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Detroit</td>\n",
       "      <td>964</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Nashville</td>\n",
       "      <td>1163</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Washington</td>\n",
       "      <td>1989</td>\n",
       "      <td>50.0</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>2299</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>918</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>El Paso</td>\n",
       "      <td>782</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>1615</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Denver</td>\n",
       "      <td>1686</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Boston</td>\n",
       "      <td>1914</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Portland</td>\n",
       "      <td>1981</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>971</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>1370</td>\n",
       "      <td>42.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>879</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Long Beach</td>\n",
       "      <td>1596</td>\n",
       "      <td>46.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Raleigh</td>\n",
       "      <td>874</td>\n",
       "      <td>44.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>1940</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Miami</td>\n",
       "      <td>2148</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Mesa</td>\n",
       "      <td>1066</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Omaha</td>\n",
       "      <td>597</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Virginia Beach</td>\n",
       "      <td>775</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Tampa</td>\n",
       "      <td>1580</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>1231</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>New Orleans</td>\n",
       "      <td>970</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Tulsa</td>\n",
       "      <td>678</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>1522</td>\n",
       "      <td>52.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>1451</td>\n",
       "      <td>48.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Wichita</td>\n",
       "      <td>492</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>1114</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>1011</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>557</td>\n",
       "      <td>25.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>892</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Jersey City</td>\n",
       "      <td>3106</td>\n",
       "      <td>60.0</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>787</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>513</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Plano</td>\n",
       "      <td>1162</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>950</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Reno</td>\n",
       "      <td>569</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>1003</td>\n",
       "      <td>37.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>1008</td>\n",
       "      <td>42.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>615</td>\n",
       "      <td>36.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>686</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Durham</td>\n",
       "      <td>613</td>\n",
       "      <td>40.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Santa Ana</td>\n",
       "      <td>1413</td>\n",
       "      <td>44.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>1135</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>1193</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>1536</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Stockton</td>\n",
       "      <td>640</td>\n",
       "      <td>28.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Toledo</td>\n",
       "      <td>530</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Orlando</td>\n",
       "      <td>1806</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>1247</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>417</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>821</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Laredo</td>\n",
       "      <td>342</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>413</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>427</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Irvine</td>\n",
       "      <td>1203</td>\n",
       "      <td>41.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Honolulu</td>\n",
       "      <td>1524</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>360</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Madison</td>\n",
       "      <td>609</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Greensboro</td>\n",
       "      <td>681</td>\n",
       "      <td>32.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>1019</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Gilbert</td>\n",
       "      <td>790</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Newark</td>\n",
       "      <td>2838</td>\n",
       "      <td>52.0</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>North Las Vegas</td>\n",
       "      <td>1059</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Lexington</td>\n",
       "      <td>561</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>1253</td>\n",
       "      <td>44.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Boise</td>\n",
       "      <td>551</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Hialeah</td>\n",
       "      <td>1069</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>905</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>531</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>821</td>\n",
       "      <td>37.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Garland</td>\n",
       "      <td>1448</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Norfolk</td>\n",
       "      <td>517</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chesapeake</td>\n",
       "      <td>488</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>971</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>1011</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Irving</td>\n",
       "      <td>1256</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>1285</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>401</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>905</td>\n",
       "      <td>34.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  Total restaurants  Diversity                 State  \\\n",
       "61     New York City               5493       61.0              New York   \n",
       "52       Los Angeles               3929       53.0            California   \n",
       "15           Chicago               3210       57.0              Illinois   \n",
       "39           Houston               2959       48.0                 Texas   \n",
       "70           Phoenix               1612       45.0               Arizona   \n",
       "69      Philadelphia               2457       52.0          Pennsylvania   \n",
       "81       San Antonio               1596       44.0                 Texas   \n",
       "82         San Diego               2350       45.0            California   \n",
       "23            Dallas               2216       51.0                 Texas   \n",
       "6             Austin               1640       50.0                 Texas   \n",
       "84          San Jose               1732       46.0            California   \n",
       "40      Indianapolis               1206       41.0               Indiana   \n",
       "29        Fort Worth               1002       38.0                 Texas   \n",
       "20          Columbus               1285       47.0                  Ohio   \n",
       "43      Jacksonville               1236       37.0               Florida   \n",
       "13         Charlotte               1584       42.0        North Carolina   \n",
       "83     San Francisco               3117       54.0            California   \n",
       "87           Seattle               2334       51.0            Washington   \n",
       "25           Detroit                964       47.0              Michigan   \n",
       "59         Nashville               1163       42.0             Tennessee   \n",
       "96        Washington               1989       50.0  District of Columbia   \n",
       "48         Las Vegas               2299       49.0                Nevada   \n",
       "66     Oklahoma City                918       35.0              Oklahoma   \n",
       "27           El Paso                782       25.0                 Texas   \n",
       "55           Memphis               1615       65.0             Tennessee   \n",
       "24            Denver               1686       49.0              Colorado   \n",
       "10            Boston               1914       52.0         Massachusetts   \n",
       "73          Portland               1981       52.0                Oregon   \n",
       "45       Kansas City                971       43.0              Missouri   \n",
       "79        Sacramento               1370       42.0            California   \n",
       "19  Colorado Springs                879       36.0              Colorado   \n",
       "51        Long Beach               1596       46.0            California   \n",
       "74           Raleigh                874       44.0        North Carolina   \n",
       "3            Atlanta               1940       44.0               Georgia   \n",
       "57             Miami               2148       50.0               Florida   \n",
       "56              Mesa               1066       43.0               Arizona   \n",
       "67             Omaha                597       33.0              Nebraska   \n",
       "95    Virginia Beach                775       34.0              Virginia   \n",
       "92             Tampa               1580       41.0               Florida   \n",
       "58       Minneapolis               1231       48.0             Minnesota   \n",
       "60       New Orleans                970       45.0             Louisiana   \n",
       "94             Tulsa                678       34.0              Oklahoma   \n",
       "65           Oakland               1522       52.0            California   \n",
       "0            Anaheim               1451       48.0            California   \n",
       "97           Wichita                492       29.0                Kansas   \n",
       "2          Arlington               1114       46.0                 Texas   \n",
       "4             Aurora               1011       47.0              Colorado   \n",
       "7        Bakersfield                557       25.0            California   \n",
       "18         Cleveland                892       45.0                  Ohio   \n",
       "44       Jersey City               3106       60.0            New Jersey   \n",
       "80        Saint Paul                787       47.0             Minnesota   \n",
       "22    Corpus Christi                513       28.0                 Texas   \n",
       "72             Plano               1162       45.0                 Texas   \n",
       "17        Cincinnati                950       38.0                  Ohio   \n",
       "75              Reno                569       31.0                Nevada   \n",
       "78         Riverside               1003       37.0            California   \n",
       "16       Chula Vista               1008       42.0            California   \n",
       "11           Buffalo                615       36.0              New York   \n",
       "12          Chandler                686       41.0               Arizona   \n",
       "26            Durham                613       40.0        North Carolina   \n",
       "85         Santa Ana               1413       44.0            California   \n",
       "86        Scottsdale               1135       45.0               Arizona   \n",
       "89         St. Louis               1193       42.0              Missouri   \n",
       "90    St. Petersburg               1536       52.0               Florida   \n",
       "91          Stockton                640       28.0            California   \n",
       "93            Toledo                530       27.0                  Ohio   \n",
       "68           Orlando               1806       45.0               Florida   \n",
       "71        Pittsburgh               1247       45.0          Pennsylvania   \n",
       "28        Fort Wayne                417       25.0               Indiana   \n",
       "33          Glendale                821       37.0               Arizona   \n",
       "47            Laredo                342       16.0                 Texas   \n",
       "1          Anchorage                413       26.0                Alaska   \n",
       "50           Lincoln                427       26.0              Nebraska   \n",
       "41            Irvine               1203       41.0            California   \n",
       "38          Honolulu               1524       40.0                Hawaii   \n",
       "53           Lubbock                360       22.0                 Texas   \n",
       "54           Madison                609       42.0             Wisconsin   \n",
       "35        Greensboro                681       32.0        North Carolina   \n",
       "36         Henderson               1019       41.0                Nevada   \n",
       "32           Gilbert                790       41.0               Arizona   \n",
       "62            Newark               2838       52.0            New Jersey   \n",
       "64   North Las Vegas               1059       39.0                Nevada   \n",
       "49         Lexington                561       30.0              Kentucky   \n",
       "30           Fremont               1253       44.0            California   \n",
       "9              Boise                551       32.0                 Idaho   \n",
       "37           Hialeah               1069       43.0               Florida   \n",
       "76          Richmond                905       34.0              Virginia   \n",
       "8        Baton Rouge                531       25.0             Louisiana   \n",
       "34          Glendale                821       37.0            California   \n",
       "31           Garland               1448       44.0                 Texas   \n",
       "63           Norfolk                517       34.0              Virginia   \n",
       "14        Chesapeake                488       30.0              Virginia   \n",
       "46       Kansas City                971       43.0                Kansas   \n",
       "5             Aurora               1011       47.0              Illinois   \n",
       "42            Irving               1256       46.0                 Texas   \n",
       "21          Columbus               1285       47.0               Georgia   \n",
       "88           Spokane                401       30.0            Washington   \n",
       "77          Richmond                905       34.0            California   \n",
       "\n",
       "    Population  \n",
       "61         8.3  \n",
       "52         4.0  \n",
       "15         2.7  \n",
       "39         2.3  \n",
       "70         1.7  \n",
       "69         1.6  \n",
       "81         1.5  \n",
       "82         1.4  \n",
       "23         1.3  \n",
       "6          1.0  \n",
       "84         1.0  \n",
       "40         0.9  \n",
       "29         0.9  \n",
       "20         0.9  \n",
       "43         0.9  \n",
       "13         0.9  \n",
       "83         0.9  \n",
       "87         0.8  \n",
       "25         0.7  \n",
       "59         0.7  \n",
       "96         0.7  \n",
       "48         0.7  \n",
       "66         0.7  \n",
       "27         0.7  \n",
       "55         0.7  \n",
       "24         0.7  \n",
       "10         0.7  \n",
       "73         0.7  \n",
       "45         0.5  \n",
       "79         0.5  \n",
       "19         0.5  \n",
       "51         0.5  \n",
       "74         0.5  \n",
       "3          0.5  \n",
       "57         0.5  \n",
       "56         0.5  \n",
       "67         0.5  \n",
       "95         0.4  \n",
       "92         0.4  \n",
       "58         0.4  \n",
       "60         0.4  \n",
       "94         0.4  \n",
       "65         0.4  \n",
       "0          0.4  \n",
       "97         0.4  \n",
       "2          0.4  \n",
       "4          0.4  \n",
       "7          0.4  \n",
       "18         0.4  \n",
       "44         0.3  \n",
       "80         0.3  \n",
       "22         0.3  \n",
       "72         0.3  \n",
       "17         0.3  \n",
       "75         0.3  \n",
       "78         0.3  \n",
       "16         0.3  \n",
       "11         0.3  \n",
       "12         0.3  \n",
       "26         0.3  \n",
       "85         0.3  \n",
       "86         0.3  \n",
       "89         0.3  \n",
       "90         0.3  \n",
       "91         0.3  \n",
       "93         0.3  \n",
       "68         0.3  \n",
       "71         0.3  \n",
       "28         0.3  \n",
       "33         0.3  \n",
       "47         0.3  \n",
       "1          0.3  \n",
       "50         0.3  \n",
       "41         0.3  \n",
       "38         0.3  \n",
       "53         0.3  \n",
       "54         0.3  \n",
       "35         0.3  \n",
       "36         0.3  \n",
       "32         0.3  \n",
       "62         0.3  \n",
       "64         0.3  \n",
       "49         0.3  \n",
       "30         0.2  \n",
       "9          0.2  \n",
       "37         0.2  \n",
       "76         0.2  \n",
       "8          0.2  \n",
       "34         0.2  \n",
       "31         0.2  \n",
       "63         0.2  \n",
       "14         0.2  \n",
       "46         0.2  \n",
       "5          0.2  \n",
       "42         0.2  \n",
       "21         0.2  \n",
       "88         0.2  \n",
       "77         0.1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cities = pd.merge(restaurants_total, df, on='City')\n",
    "df_cities.sort_values('Population', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now make some evaluation of the dataset, such as looking at the amount of restaurants against the population figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = df_cities.plot.scatter(x='Population',\n",
    "                      y='Total restaurants',\n",
    "                      c='DarkBlue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chart shows that the higher the population, the more restaurants there are in Foursquare. New York is twice as big as the following city in population, so we will take it out of the group of target cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Total restaurants</th>\n",
       "      <th>Diversity</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>1451</td>\n",
       "      <td>48.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>413</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>1114</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>1940</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>1011</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>1011</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Austin</td>\n",
       "      <td>1640</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>557</td>\n",
       "      <td>25.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>531</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Boston</td>\n",
       "      <td>1914</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>615</td>\n",
       "      <td>36.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>686</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Charlotte</td>\n",
       "      <td>1584</td>\n",
       "      <td>42.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chesapeake</td>\n",
       "      <td>488</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>3210</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>1008</td>\n",
       "      <td>42.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>950</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>892</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>879</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>1285</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>1285</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>513</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>2216</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Denver</td>\n",
       "      <td>1686</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Detroit</td>\n",
       "      <td>964</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Durham</td>\n",
       "      <td>613</td>\n",
       "      <td>40.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>El Paso</td>\n",
       "      <td>782</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>417</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>1002</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>1253</td>\n",
       "      <td>44.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Garland</td>\n",
       "      <td>1448</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Gilbert</td>\n",
       "      <td>790</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>821</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>821</td>\n",
       "      <td>37.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Greensboro</td>\n",
       "      <td>681</td>\n",
       "      <td>32.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>1019</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Hialeah</td>\n",
       "      <td>1069</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Honolulu</td>\n",
       "      <td>1524</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Houston</td>\n",
       "      <td>2959</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>1206</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Irvine</td>\n",
       "      <td>1203</td>\n",
       "      <td>41.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Irving</td>\n",
       "      <td>1256</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>1236</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Jersey City</td>\n",
       "      <td>3106</td>\n",
       "      <td>60.0</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>971</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>971</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Laredo</td>\n",
       "      <td>342</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>2299</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Lexington</td>\n",
       "      <td>561</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>427</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Long Beach</td>\n",
       "      <td>1596</td>\n",
       "      <td>46.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3929</td>\n",
       "      <td>53.0</td>\n",
       "      <td>California</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>360</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Madison</td>\n",
       "      <td>609</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>1615</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Mesa</td>\n",
       "      <td>1066</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Miami</td>\n",
       "      <td>2148</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>1231</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Nashville</td>\n",
       "      <td>1163</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>New Orleans</td>\n",
       "      <td>970</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>New York City</td>\n",
       "      <td>5493</td>\n",
       "      <td>61.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Newark</td>\n",
       "      <td>2838</td>\n",
       "      <td>52.0</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Norfolk</td>\n",
       "      <td>517</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>North Las Vegas</td>\n",
       "      <td>1059</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>1522</td>\n",
       "      <td>52.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>918</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Omaha</td>\n",
       "      <td>597</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Orlando</td>\n",
       "      <td>1806</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>2457</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>1612</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>1247</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Plano</td>\n",
       "      <td>1162</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Portland</td>\n",
       "      <td>1981</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Raleigh</td>\n",
       "      <td>874</td>\n",
       "      <td>44.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Reno</td>\n",
       "      <td>569</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>905</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>905</td>\n",
       "      <td>34.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>1003</td>\n",
       "      <td>37.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>1370</td>\n",
       "      <td>42.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>787</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>San Antonio</td>\n",
       "      <td>1596</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>2350</td>\n",
       "      <td>45.0</td>\n",
       "      <td>California</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>3117</td>\n",
       "      <td>54.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1732</td>\n",
       "      <td>46.0</td>\n",
       "      <td>California</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Santa Ana</td>\n",
       "      <td>1413</td>\n",
       "      <td>44.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>1135</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2334</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>401</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>1193</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>1536</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Stockton</td>\n",
       "      <td>640</td>\n",
       "      <td>28.0</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Tampa</td>\n",
       "      <td>1580</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Toledo</td>\n",
       "      <td>530</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Tulsa</td>\n",
       "      <td>678</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Virginia Beach</td>\n",
       "      <td>775</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Washington</td>\n",
       "      <td>1989</td>\n",
       "      <td>50.0</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Wichita</td>\n",
       "      <td>492</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  Total restaurants  Diversity                 State  \\\n",
       "0            Anaheim               1451       48.0            California   \n",
       "1          Anchorage                413       26.0                Alaska   \n",
       "2          Arlington               1114       46.0                 Texas   \n",
       "3            Atlanta               1940       44.0               Georgia   \n",
       "4             Aurora               1011       47.0              Colorado   \n",
       "5             Aurora               1011       47.0              Illinois   \n",
       "6             Austin               1640       50.0                 Texas   \n",
       "7        Bakersfield                557       25.0            California   \n",
       "8        Baton Rouge                531       25.0             Louisiana   \n",
       "10            Boston               1914       52.0         Massachusetts   \n",
       "11           Buffalo                615       36.0              New York   \n",
       "12          Chandler                686       41.0               Arizona   \n",
       "13         Charlotte               1584       42.0        North Carolina   \n",
       "14        Chesapeake                488       30.0              Virginia   \n",
       "15           Chicago               3210       57.0              Illinois   \n",
       "16       Chula Vista               1008       42.0            California   \n",
       "17        Cincinnati                950       38.0                  Ohio   \n",
       "18         Cleveland                892       45.0                  Ohio   \n",
       "19  Colorado Springs                879       36.0              Colorado   \n",
       "20          Columbus               1285       47.0                  Ohio   \n",
       "21          Columbus               1285       47.0               Georgia   \n",
       "22    Corpus Christi                513       28.0                 Texas   \n",
       "23            Dallas               2216       51.0                 Texas   \n",
       "24            Denver               1686       49.0              Colorado   \n",
       "25           Detroit                964       47.0              Michigan   \n",
       "26            Durham                613       40.0        North Carolina   \n",
       "27           El Paso                782       25.0                 Texas   \n",
       "28        Fort Wayne                417       25.0               Indiana   \n",
       "29        Fort Worth               1002       38.0                 Texas   \n",
       "30           Fremont               1253       44.0            California   \n",
       "31           Garland               1448       44.0                 Texas   \n",
       "32           Gilbert                790       41.0               Arizona   \n",
       "33          Glendale                821       37.0               Arizona   \n",
       "34          Glendale                821       37.0            California   \n",
       "35        Greensboro                681       32.0        North Carolina   \n",
       "36         Henderson               1019       41.0                Nevada   \n",
       "37           Hialeah               1069       43.0               Florida   \n",
       "38          Honolulu               1524       40.0                Hawaii   \n",
       "39           Houston               2959       48.0                 Texas   \n",
       "40      Indianapolis               1206       41.0               Indiana   \n",
       "41            Irvine               1203       41.0            California   \n",
       "42            Irving               1256       46.0                 Texas   \n",
       "43      Jacksonville               1236       37.0               Florida   \n",
       "44       Jersey City               3106       60.0            New Jersey   \n",
       "45       Kansas City                971       43.0              Missouri   \n",
       "46       Kansas City                971       43.0                Kansas   \n",
       "47            Laredo                342       16.0                 Texas   \n",
       "48         Las Vegas               2299       49.0                Nevada   \n",
       "49         Lexington                561       30.0              Kentucky   \n",
       "50           Lincoln                427       26.0              Nebraska   \n",
       "51        Long Beach               1596       46.0            California   \n",
       "52       Los Angeles               3929       53.0            California   \n",
       "53           Lubbock                360       22.0                 Texas   \n",
       "54           Madison                609       42.0             Wisconsin   \n",
       "55           Memphis               1615       65.0             Tennessee   \n",
       "56              Mesa               1066       43.0               Arizona   \n",
       "57             Miami               2148       50.0               Florida   \n",
       "58       Minneapolis               1231       48.0             Minnesota   \n",
       "59         Nashville               1163       42.0             Tennessee   \n",
       "60       New Orleans                970       45.0             Louisiana   \n",
       "61     New York City               5493       61.0              New York   \n",
       "62            Newark               2838       52.0            New Jersey   \n",
       "63           Norfolk                517       34.0              Virginia   \n",
       "64   North Las Vegas               1059       39.0                Nevada   \n",
       "65           Oakland               1522       52.0            California   \n",
       "66     Oklahoma City                918       35.0              Oklahoma   \n",
       "67             Omaha                597       33.0              Nebraska   \n",
       "68           Orlando               1806       45.0               Florida   \n",
       "69      Philadelphia               2457       52.0          Pennsylvania   \n",
       "70           Phoenix               1612       45.0               Arizona   \n",
       "71        Pittsburgh               1247       45.0          Pennsylvania   \n",
       "72             Plano               1162       45.0                 Texas   \n",
       "73          Portland               1981       52.0                Oregon   \n",
       "74           Raleigh                874       44.0        North Carolina   \n",
       "75              Reno                569       31.0                Nevada   \n",
       "76          Richmond                905       34.0              Virginia   \n",
       "77          Richmond                905       34.0            California   \n",
       "78         Riverside               1003       37.0            California   \n",
       "79        Sacramento               1370       42.0            California   \n",
       "80        Saint Paul                787       47.0             Minnesota   \n",
       "81       San Antonio               1596       44.0                 Texas   \n",
       "82         San Diego               2350       45.0            California   \n",
       "83     San Francisco               3117       54.0            California   \n",
       "84          San Jose               1732       46.0            California   \n",
       "85         Santa Ana               1413       44.0            California   \n",
       "86        Scottsdale               1135       45.0               Arizona   \n",
       "87           Seattle               2334       51.0            Washington   \n",
       "88           Spokane                401       30.0            Washington   \n",
       "89         St. Louis               1193       42.0              Missouri   \n",
       "90    St. Petersburg               1536       52.0               Florida   \n",
       "91          Stockton                640       28.0            California   \n",
       "92             Tampa               1580       41.0               Florida   \n",
       "93            Toledo                530       27.0                  Ohio   \n",
       "94             Tulsa                678       34.0              Oklahoma   \n",
       "95    Virginia Beach                775       34.0              Virginia   \n",
       "96        Washington               1989       50.0  District of Columbia   \n",
       "97           Wichita                492       29.0                Kansas   \n",
       "\n",
       "    Population  \n",
       "0          0.4  \n",
       "1          0.3  \n",
       "2          0.4  \n",
       "3          0.5  \n",
       "4          0.4  \n",
       "5          0.2  \n",
       "6          1.0  \n",
       "7          0.4  \n",
       "8          0.2  \n",
       "10         0.7  \n",
       "11         0.3  \n",
       "12         0.3  \n",
       "13         0.9  \n",
       "14         0.2  \n",
       "15         2.7  \n",
       "16         0.3  \n",
       "17         0.3  \n",
       "18         0.4  \n",
       "19         0.5  \n",
       "20         0.9  \n",
       "21         0.2  \n",
       "22         0.3  \n",
       "23         1.3  \n",
       "24         0.7  \n",
       "25         0.7  \n",
       "26         0.3  \n",
       "27         0.7  \n",
       "28         0.3  \n",
       "29         0.9  \n",
       "30         0.2  \n",
       "31         0.2  \n",
       "32         0.3  \n",
       "33         0.3  \n",
       "34         0.2  \n",
       "35         0.3  \n",
       "36         0.3  \n",
       "37         0.2  \n",
       "38         0.3  \n",
       "39         2.3  \n",
       "40         0.9  \n",
       "41         0.3  \n",
       "42         0.2  \n",
       "43         0.9  \n",
       "44         0.3  \n",
       "45         0.5  \n",
       "46         0.2  \n",
       "47         0.3  \n",
       "48         0.7  \n",
       "49         0.3  \n",
       "50         0.3  \n",
       "51         0.5  \n",
       "52         4.0  \n",
       "53         0.3  \n",
       "54         0.3  \n",
       "55         0.7  \n",
       "56         0.5  \n",
       "57         0.5  \n",
       "58         0.4  \n",
       "59         0.7  \n",
       "60         0.4  \n",
       "61         8.3  \n",
       "62         0.3  \n",
       "63         0.2  \n",
       "64         0.3  \n",
       "65         0.4  \n",
       "66         0.7  \n",
       "67         0.5  \n",
       "68         0.3  \n",
       "69         1.6  \n",
       "70         1.7  \n",
       "71         0.3  \n",
       "72         0.3  \n",
       "73         0.7  \n",
       "74         0.5  \n",
       "75         0.3  \n",
       "76         0.2  \n",
       "77         0.1  \n",
       "78         0.3  \n",
       "79         0.5  \n",
       "80         0.3  \n",
       "81         1.5  \n",
       "82         1.4  \n",
       "83         0.9  \n",
       "84         1.0  \n",
       "85         0.3  \n",
       "86         0.3  \n",
       "87         0.8  \n",
       "88         0.2  \n",
       "89         0.3  \n",
       "90         0.3  \n",
       "91         0.3  \n",
       "92         0.4  \n",
       "93         0.3  \n",
       "94         0.4  \n",
       "95         0.4  \n",
       "96         0.7  \n",
       "97         0.4  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cities.drop([9], axis = 0, inplace = True)\n",
    "df_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also delete Columbus, Georgia, since the right one is in the state of Ohio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cities.drop([3], axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = df_cities.plot.scatter(x='Population',\n",
    "                      y='Total restaurants',\n",
    "                      c='DarkBlue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the data is quite consistent, since the larger the city, more restaurants we will find. \n",
    "Let's see if this trend is the same for the Italian restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>Total restaurants</th>\n",
       "      <th>Diversity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York City</td>\n",
       "      <td>Afghan</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York City</td>\n",
       "      <td>African</td>\n",
       "      <td>91</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York City</td>\n",
       "      <td>American</td>\n",
       "      <td>375</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York City</td>\n",
       "      <td>Burmese</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York City</td>\n",
       "      <td>Cambodian</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City    Cuisine  Total restaurants  Diversity\n",
       "0  New York City     Afghan                 13       True\n",
       "1  New York City    African                 91       True\n",
       "2  New York City   American                375       True\n",
       "3  New York City    Burmese                  9       True\n",
       "4  New York City  Cambodian                  9       True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/idejuan/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py:4125: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().rename(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>Italian restaurants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>New York City</td>\n",
       "      <td>Italian</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Italian</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>Italian</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Houston</td>\n",
       "      <td>Italian</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Italian</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Italian</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>San Antonio</td>\n",
       "      <td>Italian</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>Italian</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>Italian</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>Italian</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>Austin</td>\n",
       "      <td>Italian</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>Italian</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>Italian</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>Charlotte</td>\n",
       "      <td>Italian</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Italian</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>Italian</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>Italian</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>Denver</td>\n",
       "      <td>Italian</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>Washington</td>\n",
       "      <td>Italian</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>Boston</td>\n",
       "      <td>Italian</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>El Paso</td>\n",
       "      <td>Italian</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>Nashville</td>\n",
       "      <td>Italian</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>Detroit</td>\n",
       "      <td>Italian</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>Italian</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>Portland</td>\n",
       "      <td>Italian</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>Italian</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>Italian</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1787</th>\n",
       "      <td>Mesa</td>\n",
       "      <td>Italian</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>Italian</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Italian</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>Italian</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>Italian</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>Omaha</td>\n",
       "      <td>Italian</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>Raleigh</td>\n",
       "      <td>Italian</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>Miami</td>\n",
       "      <td>Italian</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>Long Beach</td>\n",
       "      <td>Italian</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2366</th>\n",
       "      <td>Virginia Beach</td>\n",
       "      <td>Italian</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>Italian</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>Italian</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>Tulsa</td>\n",
       "      <td>Italian</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2626</th>\n",
       "      <td>Tampa</td>\n",
       "      <td>Italian</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>Italian</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2756</th>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Italian</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>Italian</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2886</th>\n",
       "      <td>Honolulu</td>\n",
       "      <td>Italian</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2951</th>\n",
       "      <td>Santa Ana</td>\n",
       "      <td>Italian</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>Italian</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>Italian</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3146</th>\n",
       "      <td>Lexington</td>\n",
       "      <td>Italian</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3211</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>Italian</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3276</th>\n",
       "      <td>Stockton</td>\n",
       "      <td>Italian</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3341</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>Italian</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>Italian</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3471</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>Italian</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Italian</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3601</th>\n",
       "      <td>Greensboro</td>\n",
       "      <td>Italian</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3666</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>Italian</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3731</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Italian</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3796</th>\n",
       "      <td>Plano</td>\n",
       "      <td>Italian</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3861</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>Italian</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>Toledo</td>\n",
       "      <td>Italian</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3988</th>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>Italian</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4053</th>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>Italian</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4118</th>\n",
       "      <td>Laredo</td>\n",
       "      <td>Italian</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4182</th>\n",
       "      <td>Jersey City</td>\n",
       "      <td>Italian</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4247</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>Italian</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4312</th>\n",
       "      <td>Madison</td>\n",
       "      <td>Italian</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4377</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>Italian</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4442</th>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>Italian</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4507</th>\n",
       "      <td>Reno</td>\n",
       "      <td>Italian</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4572</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>Italian</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4637</th>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Italian</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4702</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>Italian</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4767</th>\n",
       "      <td>North Las Vegas</td>\n",
       "      <td>Italian</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4832</th>\n",
       "      <td>Chesapeake</td>\n",
       "      <td>Italian</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>Norfolk</td>\n",
       "      <td>Italian</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4962</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>Italian</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5027</th>\n",
       "      <td>Garland</td>\n",
       "      <td>Italian</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5092</th>\n",
       "      <td>Irving</td>\n",
       "      <td>Italian</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5157</th>\n",
       "      <td>Hialeah</td>\n",
       "      <td>Italian</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5221</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>Italian</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5286</th>\n",
       "      <td>Boise</td>\n",
       "      <td>Italian</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5351</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>Italian</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5416</th>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>Italian</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5480</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>Italian</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5545</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>Italian</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5610</th>\n",
       "      <td>Wichita</td>\n",
       "      <td>Italian</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5675</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>Italian</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5740</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>Italian</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5805</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>Italian</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5870</th>\n",
       "      <td>Orlando</td>\n",
       "      <td>Italian</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5932</th>\n",
       "      <td>Irvine</td>\n",
       "      <td>Italian</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>Newark</td>\n",
       "      <td>Italian</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6055</th>\n",
       "      <td>Durham</td>\n",
       "      <td>Italian</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  City  Cuisine  Italian restaurants\n",
       "36       New York City  Italian                  334\n",
       "100        Los Angeles  Italian                  195\n",
       "165            Chicago  Italian                  214\n",
       "230            Houston  Italian                  164\n",
       "295            Phoenix  Italian                  130\n",
       "360       Philadelphia  Italian                  168\n",
       "425        San Antonio  Italian                  111\n",
       "490          San Diego  Italian                  153\n",
       "555             Dallas  Italian                  129\n",
       "620           San Jose  Italian                   56\n",
       "685             Austin  Italian                  128\n",
       "750       Jacksonville  Italian                  107\n",
       "815         Fort Worth  Italian                   63\n",
       "880          Charlotte  Italian                   92\n",
       "945      San Francisco  Italian                  167\n",
       "1010      Indianapolis  Italian                   73\n",
       "1075           Seattle  Italian                  143\n",
       "1140            Denver  Italian                  145\n",
       "1205        Washington  Italian                  140\n",
       "1270            Boston  Italian                  150\n",
       "1335           El Paso  Italian                   54\n",
       "1400         Nashville  Italian                   90\n",
       "1465           Detroit  Italian                   68\n",
       "1529     Oklahoma City  Italian                   61\n",
       "1592          Portland  Italian                  108\n",
       "1657         Las Vegas  Italian                  161\n",
       "1722           Memphis  Italian                   56\n",
       "1787              Mesa  Italian                   48\n",
       "1852        Sacramento  Italian                   45\n",
       "1916           Atlanta  Italian                  118\n",
       "1981       Kansas City  Italian                   95\n",
       "2046  Colorado Springs  Italian                   83\n",
       "2111             Omaha  Italian                   55\n",
       "2171           Raleigh  Italian                   59\n",
       "2236             Miami  Italian                  125\n",
       "2301        Long Beach  Italian                   45\n",
       "2366    Virginia Beach  Italian                   61\n",
       "2431           Oakland  Italian                   36\n",
       "2496       Minneapolis  Italian                   69\n",
       "2561             Tulsa  Italian                   35\n",
       "2626             Tampa  Italian                   81\n",
       "2691         Arlington  Italian                   24\n",
       "2756       New Orleans  Italian                   66\n",
       "2821           Anaheim  Italian                   22\n",
       "2886          Honolulu  Italian                   62\n",
       "2951         Santa Ana  Italian                   11\n",
       "3016         Riverside  Italian                   32\n",
       "3081    Corpus Christi  Italian                   25\n",
       "3146         Lexington  Italian                   34\n",
       "3211         Henderson  Italian                   41\n",
       "3276          Stockton  Italian                   17\n",
       "3341        Saint Paul  Italian                   43\n",
       "3406        Cincinnati  Italian                   48\n",
       "3471         St. Louis  Italian                  144\n",
       "3536        Pittsburgh  Italian                  129\n",
       "3601        Greensboro  Italian                   42\n",
       "3666           Lincoln  Italian                   23\n",
       "3731         Anchorage  Italian                   22\n",
       "3796             Plano  Italian                   50\n",
       "3861       Chula Vista  Italian                   11\n",
       "3926            Toledo  Italian                   64\n",
       "3988        Fort Wayne  Italian                   27\n",
       "4053    St. Petersburg  Italian                  118\n",
       "4118            Laredo  Italian                    8\n",
       "4182       Jersey City  Italian                  106\n",
       "4247          Chandler  Italian                   28\n",
       "4312           Madison  Italian                   34\n",
       "4377           Lubbock  Italian                   17\n",
       "4442        Scottsdale  Italian                   94\n",
       "4507              Reno  Italian                   33\n",
       "4572           Buffalo  Italian                   49\n",
       "4637           Gilbert  Italian                   22\n",
       "4702          Glendale  Italian                   29\n",
       "4767   North Las Vegas  Italian                   92\n",
       "4832        Chesapeake  Italian                   18\n",
       "4897           Norfolk  Italian                   23\n",
       "4962           Fremont  Italian                  169\n",
       "5027           Garland  Italian                   17\n",
       "5092            Irving  Italian                   24\n",
       "5157           Hialeah  Italian                   15\n",
       "5221          Richmond  Italian                   76\n",
       "5286             Boise  Italian                   28\n",
       "5351           Spokane  Italian                   23\n",
       "5416       Baton Rouge  Italian                   36\n",
       "5480           Memphis  Italian                   56\n",
       "5545          Columbus  Italian                   94\n",
       "5610           Wichita  Italian                   26\n",
       "5675       Bakersfield  Italian                   21\n",
       "5740         Cleveland  Italian                   66\n",
       "5805            Aurora  Italian                   68\n",
       "5870           Orlando  Italian                  103\n",
       "5932            Irvine  Italian                   25\n",
       "5993            Newark  Italian                   77\n",
       "6055            Durham  Italian                   37"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants_b = restaurants.drop('Diversity', axis = 'columns')\n",
    "italian_restaurants = restaurants_b[restaurants['Cuisine']=='Italian']\n",
    "italian_restaurants.rename(columns = {'Total restaurants':'Italian restaurants'}, inplace = True)\n",
    "italian_restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['City',\n",
       " 'Total restaurants',\n",
       " 'Diversity',\n",
       " 'State',\n",
       " 'Population',\n",
       " 'Cuisine',\n",
       " 'Italian restaurants']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cities = pd.merge(df_cities, italian_restaurants, on='City')\n",
    "list(df_cities.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Total restaurants</th>\n",
       "      <th>Italian restaurants</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Diversity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>1451</td>\n",
       "      <td>22</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>413</td>\n",
       "      <td>22</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>1114</td>\n",
       "      <td>24</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.4</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>1011</td>\n",
       "      <td>68</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.4</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>1011</td>\n",
       "      <td>68</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>0.2</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Austin</td>\n",
       "      <td>1640</td>\n",
       "      <td>128</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>557</td>\n",
       "      <td>21</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>531</td>\n",
       "      <td>36</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.2</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Boston</td>\n",
       "      <td>1914</td>\n",
       "      <td>150</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>0.7</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>615</td>\n",
       "      <td>49</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.3</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>686</td>\n",
       "      <td>28</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Charlotte</td>\n",
       "      <td>1584</td>\n",
       "      <td>92</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.9</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chesapeake</td>\n",
       "      <td>488</td>\n",
       "      <td>18</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>3210</td>\n",
       "      <td>214</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>2.7</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>1008</td>\n",
       "      <td>11</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>950</td>\n",
       "      <td>48</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.3</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>892</td>\n",
       "      <td>66</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.4</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>879</td>\n",
       "      <td>83</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.5</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>1285</td>\n",
       "      <td>94</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.9</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>1285</td>\n",
       "      <td>94</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0.2</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>513</td>\n",
       "      <td>25</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>2216</td>\n",
       "      <td>129</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.3</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Denver</td>\n",
       "      <td>1686</td>\n",
       "      <td>145</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.7</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Detroit</td>\n",
       "      <td>964</td>\n",
       "      <td>68</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>0.7</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Durham</td>\n",
       "      <td>613</td>\n",
       "      <td>37</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.3</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>El Paso</td>\n",
       "      <td>782</td>\n",
       "      <td>54</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.7</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>417</td>\n",
       "      <td>27</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.3</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>1002</td>\n",
       "      <td>63</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.9</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>1253</td>\n",
       "      <td>169</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Garland</td>\n",
       "      <td>1448</td>\n",
       "      <td>17</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.2</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Gilbert</td>\n",
       "      <td>790</td>\n",
       "      <td>22</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>821</td>\n",
       "      <td>29</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>821</td>\n",
       "      <td>29</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Greensboro</td>\n",
       "      <td>681</td>\n",
       "      <td>42</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>1019</td>\n",
       "      <td>41</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Hialeah</td>\n",
       "      <td>1069</td>\n",
       "      <td>15</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Honolulu</td>\n",
       "      <td>1524</td>\n",
       "      <td>62</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>0.3</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Houston</td>\n",
       "      <td>2959</td>\n",
       "      <td>164</td>\n",
       "      <td>Texas</td>\n",
       "      <td>2.3</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>1206</td>\n",
       "      <td>73</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.9</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Irvine</td>\n",
       "      <td>1203</td>\n",
       "      <td>25</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Irving</td>\n",
       "      <td>1256</td>\n",
       "      <td>24</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.2</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>1236</td>\n",
       "      <td>107</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.9</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Jersey City</td>\n",
       "      <td>3106</td>\n",
       "      <td>106</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.3</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>971</td>\n",
       "      <td>95</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.5</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>971</td>\n",
       "      <td>95</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Laredo</td>\n",
       "      <td>342</td>\n",
       "      <td>8</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>2299</td>\n",
       "      <td>161</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.7</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Lexington</td>\n",
       "      <td>561</td>\n",
       "      <td>34</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>427</td>\n",
       "      <td>23</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Long Beach</td>\n",
       "      <td>1596</td>\n",
       "      <td>45</td>\n",
       "      <td>California</td>\n",
       "      <td>0.5</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3929</td>\n",
       "      <td>195</td>\n",
       "      <td>California</td>\n",
       "      <td>4.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>360</td>\n",
       "      <td>17</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Madison</td>\n",
       "      <td>609</td>\n",
       "      <td>34</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.3</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>1615</td>\n",
       "      <td>56</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>1615</td>\n",
       "      <td>56</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Mesa</td>\n",
       "      <td>1066</td>\n",
       "      <td>48</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.5</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Miami</td>\n",
       "      <td>2148</td>\n",
       "      <td>125</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.5</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>1231</td>\n",
       "      <td>69</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Nashville</td>\n",
       "      <td>1163</td>\n",
       "      <td>90</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>New Orleans</td>\n",
       "      <td>970</td>\n",
       "      <td>66</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.4</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>New York City</td>\n",
       "      <td>5493</td>\n",
       "      <td>334</td>\n",
       "      <td>New York</td>\n",
       "      <td>8.3</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Newark</td>\n",
       "      <td>2838</td>\n",
       "      <td>77</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Norfolk</td>\n",
       "      <td>517</td>\n",
       "      <td>23</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>North Las Vegas</td>\n",
       "      <td>1059</td>\n",
       "      <td>92</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>1522</td>\n",
       "      <td>36</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>918</td>\n",
       "      <td>61</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.7</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Omaha</td>\n",
       "      <td>597</td>\n",
       "      <td>55</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.5</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Orlando</td>\n",
       "      <td>1806</td>\n",
       "      <td>103</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>2457</td>\n",
       "      <td>168</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>1.6</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>1612</td>\n",
       "      <td>130</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1.7</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>1247</td>\n",
       "      <td>129</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Plano</td>\n",
       "      <td>1162</td>\n",
       "      <td>50</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Portland</td>\n",
       "      <td>1981</td>\n",
       "      <td>108</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.7</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Raleigh</td>\n",
       "      <td>874</td>\n",
       "      <td>59</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Reno</td>\n",
       "      <td>569</td>\n",
       "      <td>33</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>905</td>\n",
       "      <td>76</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>905</td>\n",
       "      <td>76</td>\n",
       "      <td>California</td>\n",
       "      <td>0.1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>1003</td>\n",
       "      <td>32</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>1370</td>\n",
       "      <td>45</td>\n",
       "      <td>California</td>\n",
       "      <td>0.5</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>787</td>\n",
       "      <td>43</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.3</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>San Antonio</td>\n",
       "      <td>1596</td>\n",
       "      <td>111</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>2350</td>\n",
       "      <td>153</td>\n",
       "      <td>California</td>\n",
       "      <td>1.4</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>3117</td>\n",
       "      <td>167</td>\n",
       "      <td>California</td>\n",
       "      <td>0.9</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1732</td>\n",
       "      <td>56</td>\n",
       "      <td>California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Santa Ana</td>\n",
       "      <td>1413</td>\n",
       "      <td>11</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>1135</td>\n",
       "      <td>94</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2334</td>\n",
       "      <td>143</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.8</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>401</td>\n",
       "      <td>23</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.2</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>1193</td>\n",
       "      <td>144</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.3</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>1536</td>\n",
       "      <td>118</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Stockton</td>\n",
       "      <td>640</td>\n",
       "      <td>17</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Tampa</td>\n",
       "      <td>1580</td>\n",
       "      <td>81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.4</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Toledo</td>\n",
       "      <td>530</td>\n",
       "      <td>64</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Tulsa</td>\n",
       "      <td>678</td>\n",
       "      <td>35</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.4</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Virginia Beach</td>\n",
       "      <td>775</td>\n",
       "      <td>61</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.4</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Washington</td>\n",
       "      <td>1989</td>\n",
       "      <td>140</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>0.7</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Wichita</td>\n",
       "      <td>492</td>\n",
       "      <td>26</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.4</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  Total restaurants  Italian restaurants  \\\n",
       "0            Anaheim               1451                   22   \n",
       "1          Anchorage                413                   22   \n",
       "2          Arlington               1114                   24   \n",
       "3             Aurora               1011                   68   \n",
       "4             Aurora               1011                   68   \n",
       "5             Austin               1640                  128   \n",
       "6        Bakersfield                557                   21   \n",
       "7        Baton Rouge                531                   36   \n",
       "8             Boston               1914                  150   \n",
       "9            Buffalo                615                   49   \n",
       "10          Chandler                686                   28   \n",
       "11         Charlotte               1584                   92   \n",
       "12        Chesapeake                488                   18   \n",
       "13           Chicago               3210                  214   \n",
       "14       Chula Vista               1008                   11   \n",
       "15        Cincinnati                950                   48   \n",
       "16         Cleveland                892                   66   \n",
       "17  Colorado Springs                879                   83   \n",
       "18          Columbus               1285                   94   \n",
       "19          Columbus               1285                   94   \n",
       "20    Corpus Christi                513                   25   \n",
       "21            Dallas               2216                  129   \n",
       "22            Denver               1686                  145   \n",
       "23           Detroit                964                   68   \n",
       "24            Durham                613                   37   \n",
       "25           El Paso                782                   54   \n",
       "26        Fort Wayne                417                   27   \n",
       "27        Fort Worth               1002                   63   \n",
       "28           Fremont               1253                  169   \n",
       "29           Garland               1448                   17   \n",
       "30           Gilbert                790                   22   \n",
       "31          Glendale                821                   29   \n",
       "32          Glendale                821                   29   \n",
       "33        Greensboro                681                   42   \n",
       "34         Henderson               1019                   41   \n",
       "35           Hialeah               1069                   15   \n",
       "36          Honolulu               1524                   62   \n",
       "37           Houston               2959                  164   \n",
       "38      Indianapolis               1206                   73   \n",
       "39            Irvine               1203                   25   \n",
       "40            Irving               1256                   24   \n",
       "41      Jacksonville               1236                  107   \n",
       "42       Jersey City               3106                  106   \n",
       "43       Kansas City                971                   95   \n",
       "44       Kansas City                971                   95   \n",
       "45            Laredo                342                    8   \n",
       "46         Las Vegas               2299                  161   \n",
       "47         Lexington                561                   34   \n",
       "48           Lincoln                427                   23   \n",
       "49        Long Beach               1596                   45   \n",
       "50       Los Angeles               3929                  195   \n",
       "51           Lubbock                360                   17   \n",
       "52           Madison                609                   34   \n",
       "53           Memphis               1615                   56   \n",
       "54           Memphis               1615                   56   \n",
       "55              Mesa               1066                   48   \n",
       "56             Miami               2148                  125   \n",
       "57       Minneapolis               1231                   69   \n",
       "58         Nashville               1163                   90   \n",
       "59       New Orleans                970                   66   \n",
       "60     New York City               5493                  334   \n",
       "61            Newark               2838                   77   \n",
       "62           Norfolk                517                   23   \n",
       "63   North Las Vegas               1059                   92   \n",
       "64           Oakland               1522                   36   \n",
       "65     Oklahoma City                918                   61   \n",
       "66             Omaha                597                   55   \n",
       "67           Orlando               1806                  103   \n",
       "68      Philadelphia               2457                  168   \n",
       "69           Phoenix               1612                  130   \n",
       "70        Pittsburgh               1247                  129   \n",
       "71             Plano               1162                   50   \n",
       "72          Portland               1981                  108   \n",
       "73           Raleigh                874                   59   \n",
       "74              Reno                569                   33   \n",
       "75          Richmond                905                   76   \n",
       "76          Richmond                905                   76   \n",
       "77         Riverside               1003                   32   \n",
       "78        Sacramento               1370                   45   \n",
       "79        Saint Paul                787                   43   \n",
       "80       San Antonio               1596                  111   \n",
       "81         San Diego               2350                  153   \n",
       "82     San Francisco               3117                  167   \n",
       "83          San Jose               1732                   56   \n",
       "84         Santa Ana               1413                   11   \n",
       "85        Scottsdale               1135                   94   \n",
       "86           Seattle               2334                  143   \n",
       "87           Spokane                401                   23   \n",
       "88         St. Louis               1193                  144   \n",
       "89    St. Petersburg               1536                  118   \n",
       "90          Stockton                640                   17   \n",
       "91             Tampa               1580                   81   \n",
       "92            Toledo                530                   64   \n",
       "93             Tulsa                678                   35   \n",
       "94    Virginia Beach                775                   61   \n",
       "95        Washington               1989                  140   \n",
       "96           Wichita                492                   26   \n",
       "\n",
       "                   State  Population  Diversity  \n",
       "0             California         0.4       48.0  \n",
       "1                 Alaska         0.3       26.0  \n",
       "2                  Texas         0.4       46.0  \n",
       "3               Colorado         0.4       47.0  \n",
       "4               Illinois         0.2       47.0  \n",
       "5                  Texas         1.0       50.0  \n",
       "6             California         0.4       25.0  \n",
       "7              Louisiana         0.2       25.0  \n",
       "8          Massachusetts         0.7       52.0  \n",
       "9               New York         0.3       36.0  \n",
       "10               Arizona         0.3       41.0  \n",
       "11        North Carolina         0.9       42.0  \n",
       "12              Virginia         0.2       30.0  \n",
       "13              Illinois         2.7       57.0  \n",
       "14            California         0.3       42.0  \n",
       "15                  Ohio         0.3       38.0  \n",
       "16                  Ohio         0.4       45.0  \n",
       "17              Colorado         0.5       36.0  \n",
       "18                  Ohio         0.9       47.0  \n",
       "19               Georgia         0.2       47.0  \n",
       "20                 Texas         0.3       28.0  \n",
       "21                 Texas         1.3       51.0  \n",
       "22              Colorado         0.7       49.0  \n",
       "23              Michigan         0.7       47.0  \n",
       "24        North Carolina         0.3       40.0  \n",
       "25                 Texas         0.7       25.0  \n",
       "26               Indiana         0.3       25.0  \n",
       "27                 Texas         0.9       38.0  \n",
       "28            California         0.2       44.0  \n",
       "29                 Texas         0.2       44.0  \n",
       "30               Arizona         0.3       41.0  \n",
       "31               Arizona         0.3       37.0  \n",
       "32            California         0.2       37.0  \n",
       "33        North Carolina         0.3       32.0  \n",
       "34                Nevada         0.3       41.0  \n",
       "35               Florida         0.2       43.0  \n",
       "36                Hawaii         0.3       40.0  \n",
       "37                 Texas         2.3       48.0  \n",
       "38               Indiana         0.9       41.0  \n",
       "39            California         0.3       41.0  \n",
       "40                 Texas         0.2       46.0  \n",
       "41               Florida         0.9       37.0  \n",
       "42            New Jersey         0.3       60.0  \n",
       "43              Missouri         0.5       43.0  \n",
       "44                Kansas         0.2       43.0  \n",
       "45                 Texas         0.3       16.0  \n",
       "46                Nevada         0.7       49.0  \n",
       "47              Kentucky         0.3       30.0  \n",
       "48              Nebraska         0.3       26.0  \n",
       "49            California         0.5       46.0  \n",
       "50            California         4.0       53.0  \n",
       "51                 Texas         0.3       22.0  \n",
       "52             Wisconsin         0.3       42.0  \n",
       "53             Tennessee         0.7       65.0  \n",
       "54             Tennessee         0.7       65.0  \n",
       "55               Arizona         0.5       43.0  \n",
       "56               Florida         0.5       50.0  \n",
       "57             Minnesota         0.4       48.0  \n",
       "58             Tennessee         0.7       42.0  \n",
       "59             Louisiana         0.4       45.0  \n",
       "60              New York         8.3       61.0  \n",
       "61            New Jersey         0.3       52.0  \n",
       "62              Virginia         0.2       34.0  \n",
       "63                Nevada         0.3       39.0  \n",
       "64            California         0.4       52.0  \n",
       "65              Oklahoma         0.7       35.0  \n",
       "66              Nebraska         0.5       33.0  \n",
       "67               Florida         0.3       45.0  \n",
       "68          Pennsylvania         1.6       52.0  \n",
       "69               Arizona         1.7       45.0  \n",
       "70          Pennsylvania         0.3       45.0  \n",
       "71                 Texas         0.3       45.0  \n",
       "72                Oregon         0.7       52.0  \n",
       "73        North Carolina         0.5       44.0  \n",
       "74                Nevada         0.3       31.0  \n",
       "75              Virginia         0.2       34.0  \n",
       "76            California         0.1       34.0  \n",
       "77            California         0.3       37.0  \n",
       "78            California         0.5       42.0  \n",
       "79             Minnesota         0.3       47.0  \n",
       "80                 Texas         1.5       44.0  \n",
       "81            California         1.4       45.0  \n",
       "82            California         0.9       54.0  \n",
       "83            California         1.0       46.0  \n",
       "84            California         0.3       44.0  \n",
       "85               Arizona         0.3       45.0  \n",
       "86            Washington         0.8       51.0  \n",
       "87            Washington         0.2       30.0  \n",
       "88              Missouri         0.3       42.0  \n",
       "89               Florida         0.3       52.0  \n",
       "90            California         0.3       28.0  \n",
       "91               Florida         0.4       41.0  \n",
       "92                  Ohio         0.3       27.0  \n",
       "93              Oklahoma         0.4       34.0  \n",
       "94              Virginia         0.4       34.0  \n",
       "95  District of Columbia         0.7       50.0  \n",
       "96                Kansas         0.4       29.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cities = df_cities[[\"City\",\"Total restaurants\",\"Italian restaurants\",\"State\",\"Population\",\"Diversity\"]]\n",
    "df_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "# Divide the figure into a 1x2 grid, and give me the first section\n",
    "ax1 = fig.add_subplot(121)\n",
    "\n",
    "# Divide the figure into a 1x2 grid, and give me the second section\n",
    "ax2 = fig.add_subplot(122)\n",
    "\n",
    "\n",
    "ax1 = df_cities.plot.scatter(x='Population',\n",
    "                      y='Total restaurants',\n",
    "                      c='DarkBlue',figsize = (15,5), ax = ax1)\n",
    "\n",
    "ax2 = df_cities.plot.scatter(x='Population',\n",
    "                      y='Italian restaurants',\n",
    "                      c='Red',figsize = (15,5), ax = ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the percentage of population from Italian descent. \n",
    "Source: https://en.wikipedia.org/wiki/Italian_Americans#U.S._states_with_over_10%_people_of_Italian_ancestry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Italian origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Italian origin\n",
       "0     Alabama             1.7\n",
       "1      Alaska             3.3\n",
       "2     Arizona             4.4\n",
       "3    Arkansas             1.6\n",
       "4  California             4.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "italian_origin = {'Alabama' : 1.7,\n",
    "            'Alaska' : 3.3,\n",
    "            'Arizona' : 4.4,\n",
    "            'Arkansas' : 1.6,\n",
    "            'California' : 4.0,\n",
    "            'Colorado' : 5.1,\n",
    "            'Connecticut' : 18.7,\n",
    "            'Delaware': 9.9,\n",
    "            'Florida' : 6.2,\n",
    "            'Georgia' : 2.2,\n",
    "            'Hawaii' : 2.1,\n",
    "            'Idaho' : 3.2,\n",
    "            'Illinois' : 6.2,\n",
    "            'Indiana' : 2.7,\n",
    "            'Iowa' : 2.1,\n",
    "            'Kansas' : 2.3,\n",
    "            'Kentucky' : 2.0,\n",
    "            'Louisiana' : 4.8,\n",
    "            'Maine' : 5.6,\n",
    "            'Maryland' : 5.2,\n",
    "            'Massachusetts' : 13.9,\n",
    "            'Michigan' : 4.7,\n",
    "            'Minnesota' : 2.3,\n",
    "            'Mississippi' : 1.8,\n",
    "            'Missouri' : 3.4,\n",
    "            'Montana' : 3.5,\n",
    "            'Nebraska' : 2.8,\n",
    "            'Nevada' : 6.3,\n",
    "            'New Hampshire' : 10.3,\n",
    "            'New Jersey' : 16.8,\n",
    "            'New Mexico' : 2.4,\n",
    "            'New York' : 13.5,\n",
    "            'North Carolina' : 3.0,\n",
    "            'North Dakota' : 1.2,\n",
    "            'Ohio' : 6.4,\n",
    "            'Oklahoma' : 1.7,\n",
    "            'Oregon' : 3.8,\n",
    "            'Pennsylvania' : 12.2,\n",
    "            'Rhode Island' : 18.9,\n",
    "            'South Carolina' : 2.9,\n",
    "            'South Dakota' : 1.4,\n",
    "            'Tennessee' : 2.2,\n",
    "            'Texas' : 1.9,\n",
    "            'Utah' : 2.7,\n",
    "            'Vermont' : 7.4,\n",
    "            'Virginia' : 4.0,\n",
    "            'Washington' : 3.6,\n",
    "            'West Virginia' : 4.7,\n",
    "            'Wisconsin' : 3.5,\n",
    "            'Wyoming' : 3.1,\n",
    "            'District of Columbia' : 3.3}\n",
    "\n",
    "\n",
    "df_italian_origin = pd.DataFrame(list(italian_origin.items()),columns = ['State','Italian origin']) \n",
    "df_italian_origin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Total restaurants</th>\n",
       "      <th>Italian restaurants</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Diversity</th>\n",
       "      <th>Italian origin</th>\n",
       "      <th>italian_rest_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>1451</td>\n",
       "      <td>22</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>557</td>\n",
       "      <td>21</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>1008</td>\n",
       "      <td>11</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>1253</td>\n",
       "      <td>169</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>821</td>\n",
       "      <td>29</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Irvine</td>\n",
       "      <td>1203</td>\n",
       "      <td>25</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Long Beach</td>\n",
       "      <td>1596</td>\n",
       "      <td>45</td>\n",
       "      <td>California</td>\n",
       "      <td>0.5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3929</td>\n",
       "      <td>195</td>\n",
       "      <td>California</td>\n",
       "      <td>4.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>1522</td>\n",
       "      <td>36</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>905</td>\n",
       "      <td>76</td>\n",
       "      <td>California</td>\n",
       "      <td>0.1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>1003</td>\n",
       "      <td>32</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>1370</td>\n",
       "      <td>45</td>\n",
       "      <td>California</td>\n",
       "      <td>0.5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>2350</td>\n",
       "      <td>153</td>\n",
       "      <td>California</td>\n",
       "      <td>1.4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>3117</td>\n",
       "      <td>167</td>\n",
       "      <td>California</td>\n",
       "      <td>0.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1732</td>\n",
       "      <td>56</td>\n",
       "      <td>California</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Santa Ana</td>\n",
       "      <td>1413</td>\n",
       "      <td>11</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stockton</td>\n",
       "      <td>640</td>\n",
       "      <td>17</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>413</td>\n",
       "      <td>22</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>1114</td>\n",
       "      <td>24</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.4</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Austin</td>\n",
       "      <td>1640</td>\n",
       "      <td>128</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>513</td>\n",
       "      <td>25</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>2216</td>\n",
       "      <td>129</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.3</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>El Paso</td>\n",
       "      <td>782</td>\n",
       "      <td>54</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>1002</td>\n",
       "      <td>63</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.9</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Garland</td>\n",
       "      <td>1448</td>\n",
       "      <td>17</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Houston</td>\n",
       "      <td>2959</td>\n",
       "      <td>164</td>\n",
       "      <td>Texas</td>\n",
       "      <td>2.3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Irving</td>\n",
       "      <td>1256</td>\n",
       "      <td>24</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.2</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Laredo</td>\n",
       "      <td>342</td>\n",
       "      <td>8</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>360</td>\n",
       "      <td>17</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Plano</td>\n",
       "      <td>1162</td>\n",
       "      <td>50</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>San Antonio</td>\n",
       "      <td>1596</td>\n",
       "      <td>111</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.5</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>1011</td>\n",
       "      <td>68</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.4</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>879</td>\n",
       "      <td>83</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Denver</td>\n",
       "      <td>1686</td>\n",
       "      <td>145</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.7</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>1011</td>\n",
       "      <td>68</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>0.2</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>3210</td>\n",
       "      <td>214</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>2.7</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>531</td>\n",
       "      <td>36</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>New Orleans</td>\n",
       "      <td>970</td>\n",
       "      <td>66</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Boston</td>\n",
       "      <td>1914</td>\n",
       "      <td>150</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>0.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>615</td>\n",
       "      <td>49</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.3</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>New York City</td>\n",
       "      <td>5493</td>\n",
       "      <td>334</td>\n",
       "      <td>New York</td>\n",
       "      <td>8.3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>686</td>\n",
       "      <td>28</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Gilbert</td>\n",
       "      <td>790</td>\n",
       "      <td>22</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>821</td>\n",
       "      <td>29</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Mesa</td>\n",
       "      <td>1066</td>\n",
       "      <td>48</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>1612</td>\n",
       "      <td>130</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1.7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>1135</td>\n",
       "      <td>94</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Charlotte</td>\n",
       "      <td>1584</td>\n",
       "      <td>92</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.9</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Durham</td>\n",
       "      <td>613</td>\n",
       "      <td>37</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Greensboro</td>\n",
       "      <td>681</td>\n",
       "      <td>42</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Raleigh</td>\n",
       "      <td>874</td>\n",
       "      <td>59</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.5</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Chesapeake</td>\n",
       "      <td>488</td>\n",
       "      <td>18</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Norfolk</td>\n",
       "      <td>517</td>\n",
       "      <td>23</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>905</td>\n",
       "      <td>76</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Virginia Beach</td>\n",
       "      <td>775</td>\n",
       "      <td>61</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.4</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>950</td>\n",
       "      <td>48</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>892</td>\n",
       "      <td>66</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>1285</td>\n",
       "      <td>94</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.9</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Toledo</td>\n",
       "      <td>530</td>\n",
       "      <td>64</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>1285</td>\n",
       "      <td>94</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0.2</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Detroit</td>\n",
       "      <td>964</td>\n",
       "      <td>68</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>0.7</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>417</td>\n",
       "      <td>27</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>1206</td>\n",
       "      <td>73</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.9</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>1019</td>\n",
       "      <td>41</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>2299</td>\n",
       "      <td>161</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.7</td>\n",
       "      <td>49.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>North Las Vegas</td>\n",
       "      <td>1059</td>\n",
       "      <td>92</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Reno</td>\n",
       "      <td>569</td>\n",
       "      <td>33</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Hialeah</td>\n",
       "      <td>1069</td>\n",
       "      <td>15</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>1236</td>\n",
       "      <td>107</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.9</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Miami</td>\n",
       "      <td>2148</td>\n",
       "      <td>125</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Orlando</td>\n",
       "      <td>1806</td>\n",
       "      <td>103</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>1536</td>\n",
       "      <td>118</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Tampa</td>\n",
       "      <td>1580</td>\n",
       "      <td>81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Honolulu</td>\n",
       "      <td>1524</td>\n",
       "      <td>62</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>0.3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Jersey City</td>\n",
       "      <td>3106</td>\n",
       "      <td>106</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.3</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Newark</td>\n",
       "      <td>2838</td>\n",
       "      <td>77</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.3</td>\n",
       "      <td>52.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>971</td>\n",
       "      <td>95</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>1193</td>\n",
       "      <td>144</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>971</td>\n",
       "      <td>95</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Wichita</td>\n",
       "      <td>492</td>\n",
       "      <td>26</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.4</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Lexington</td>\n",
       "      <td>561</td>\n",
       "      <td>34</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>427</td>\n",
       "      <td>23</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Omaha</td>\n",
       "      <td>597</td>\n",
       "      <td>55</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Madison</td>\n",
       "      <td>609</td>\n",
       "      <td>34</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>1615</td>\n",
       "      <td>56</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>1615</td>\n",
       "      <td>56</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Nashville</td>\n",
       "      <td>1163</td>\n",
       "      <td>90</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.7</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>1231</td>\n",
       "      <td>69</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>787</td>\n",
       "      <td>43</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>918</td>\n",
       "      <td>61</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.7</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Tulsa</td>\n",
       "      <td>678</td>\n",
       "      <td>35</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.4</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>2457</td>\n",
       "      <td>168</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>1.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>1247</td>\n",
       "      <td>129</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Portland</td>\n",
       "      <td>1981</td>\n",
       "      <td>108</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2334</td>\n",
       "      <td>143</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.8</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>401</td>\n",
       "      <td>23</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Washington</td>\n",
       "      <td>1989</td>\n",
       "      <td>140</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>0.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  Total restaurants  Italian restaurants  \\\n",
       "0            Anaheim               1451                   22   \n",
       "1        Bakersfield                557                   21   \n",
       "2        Chula Vista               1008                   11   \n",
       "3            Fremont               1253                  169   \n",
       "4           Glendale                821                   29   \n",
       "5             Irvine               1203                   25   \n",
       "6         Long Beach               1596                   45   \n",
       "7        Los Angeles               3929                  195   \n",
       "8            Oakland               1522                   36   \n",
       "9           Richmond                905                   76   \n",
       "10         Riverside               1003                   32   \n",
       "11        Sacramento               1370                   45   \n",
       "12         San Diego               2350                  153   \n",
       "13     San Francisco               3117                  167   \n",
       "14          San Jose               1732                   56   \n",
       "15         Santa Ana               1413                   11   \n",
       "16          Stockton                640                   17   \n",
       "17         Anchorage                413                   22   \n",
       "18         Arlington               1114                   24   \n",
       "19            Austin               1640                  128   \n",
       "20    Corpus Christi                513                   25   \n",
       "21            Dallas               2216                  129   \n",
       "22           El Paso                782                   54   \n",
       "23        Fort Worth               1002                   63   \n",
       "24           Garland               1448                   17   \n",
       "25           Houston               2959                  164   \n",
       "26            Irving               1256                   24   \n",
       "27            Laredo                342                    8   \n",
       "28           Lubbock                360                   17   \n",
       "29             Plano               1162                   50   \n",
       "30       San Antonio               1596                  111   \n",
       "31            Aurora               1011                   68   \n",
       "32  Colorado Springs                879                   83   \n",
       "33            Denver               1686                  145   \n",
       "34            Aurora               1011                   68   \n",
       "35           Chicago               3210                  214   \n",
       "36       Baton Rouge                531                   36   \n",
       "37       New Orleans                970                   66   \n",
       "38            Boston               1914                  150   \n",
       "39           Buffalo                615                   49   \n",
       "40     New York City               5493                  334   \n",
       "41          Chandler                686                   28   \n",
       "42           Gilbert                790                   22   \n",
       "43          Glendale                821                   29   \n",
       "44              Mesa               1066                   48   \n",
       "45           Phoenix               1612                  130   \n",
       "46        Scottsdale               1135                   94   \n",
       "47         Charlotte               1584                   92   \n",
       "48            Durham                613                   37   \n",
       "49        Greensboro                681                   42   \n",
       "50           Raleigh                874                   59   \n",
       "51        Chesapeake                488                   18   \n",
       "52           Norfolk                517                   23   \n",
       "53          Richmond                905                   76   \n",
       "54    Virginia Beach                775                   61   \n",
       "55        Cincinnati                950                   48   \n",
       "56         Cleveland                892                   66   \n",
       "57          Columbus               1285                   94   \n",
       "58            Toledo                530                   64   \n",
       "59          Columbus               1285                   94   \n",
       "60           Detroit                964                   68   \n",
       "61        Fort Wayne                417                   27   \n",
       "62      Indianapolis               1206                   73   \n",
       "63         Henderson               1019                   41   \n",
       "64         Las Vegas               2299                  161   \n",
       "65   North Las Vegas               1059                   92   \n",
       "66              Reno                569                   33   \n",
       "67           Hialeah               1069                   15   \n",
       "68      Jacksonville               1236                  107   \n",
       "69             Miami               2148                  125   \n",
       "70           Orlando               1806                  103   \n",
       "71    St. Petersburg               1536                  118   \n",
       "72             Tampa               1580                   81   \n",
       "73          Honolulu               1524                   62   \n",
       "74       Jersey City               3106                  106   \n",
       "75            Newark               2838                   77   \n",
       "76       Kansas City                971                   95   \n",
       "77         St. Louis               1193                  144   \n",
       "78       Kansas City                971                   95   \n",
       "79           Wichita                492                   26   \n",
       "80         Lexington                561                   34   \n",
       "81           Lincoln                427                   23   \n",
       "82             Omaha                597                   55   \n",
       "83           Madison                609                   34   \n",
       "84           Memphis               1615                   56   \n",
       "85           Memphis               1615                   56   \n",
       "86         Nashville               1163                   90   \n",
       "87       Minneapolis               1231                   69   \n",
       "88        Saint Paul                787                   43   \n",
       "89     Oklahoma City                918                   61   \n",
       "90             Tulsa                678                   35   \n",
       "91      Philadelphia               2457                  168   \n",
       "92        Pittsburgh               1247                  129   \n",
       "93          Portland               1981                  108   \n",
       "94           Seattle               2334                  143   \n",
       "95           Spokane                401                   23   \n",
       "96        Washington               1989                  140   \n",
       "\n",
       "                   State  Population  Diversity  Italian origin  \\\n",
       "0             California         0.4       48.0             4.0   \n",
       "1             California         0.4       25.0             4.0   \n",
       "2             California         0.3       42.0             4.0   \n",
       "3             California         0.2       44.0             4.0   \n",
       "4             California         0.2       37.0             4.0   \n",
       "5             California         0.3       41.0             4.0   \n",
       "6             California         0.5       46.0             4.0   \n",
       "7             California         4.0       53.0             4.0   \n",
       "8             California         0.4       52.0             4.0   \n",
       "9             California         0.1       34.0             4.0   \n",
       "10            California         0.3       37.0             4.0   \n",
       "11            California         0.5       42.0             4.0   \n",
       "12            California         1.4       45.0             4.0   \n",
       "13            California         0.9       54.0             4.0   \n",
       "14            California         1.0       46.0             4.0   \n",
       "15            California         0.3       44.0             4.0   \n",
       "16            California         0.3       28.0             4.0   \n",
       "17                Alaska         0.3       26.0             3.3   \n",
       "18                 Texas         0.4       46.0             1.9   \n",
       "19                 Texas         1.0       50.0             1.9   \n",
       "20                 Texas         0.3       28.0             1.9   \n",
       "21                 Texas         1.3       51.0             1.9   \n",
       "22                 Texas         0.7       25.0             1.9   \n",
       "23                 Texas         0.9       38.0             1.9   \n",
       "24                 Texas         0.2       44.0             1.9   \n",
       "25                 Texas         2.3       48.0             1.9   \n",
       "26                 Texas         0.2       46.0             1.9   \n",
       "27                 Texas         0.3       16.0             1.9   \n",
       "28                 Texas         0.3       22.0             1.9   \n",
       "29                 Texas         0.3       45.0             1.9   \n",
       "30                 Texas         1.5       44.0             1.9   \n",
       "31              Colorado         0.4       47.0             5.1   \n",
       "32              Colorado         0.5       36.0             5.1   \n",
       "33              Colorado         0.7       49.0             5.1   \n",
       "34              Illinois         0.2       47.0             6.2   \n",
       "35              Illinois         2.7       57.0             6.2   \n",
       "36             Louisiana         0.2       25.0             4.8   \n",
       "37             Louisiana         0.4       45.0             4.8   \n",
       "38         Massachusetts         0.7       52.0            13.9   \n",
       "39              New York         0.3       36.0            13.5   \n",
       "40              New York         8.3       61.0            13.5   \n",
       "41               Arizona         0.3       41.0             4.4   \n",
       "42               Arizona         0.3       41.0             4.4   \n",
       "43               Arizona         0.3       37.0             4.4   \n",
       "44               Arizona         0.5       43.0             4.4   \n",
       "45               Arizona         1.7       45.0             4.4   \n",
       "46               Arizona         0.3       45.0             4.4   \n",
       "47        North Carolina         0.9       42.0             3.0   \n",
       "48        North Carolina         0.3       40.0             3.0   \n",
       "49        North Carolina         0.3       32.0             3.0   \n",
       "50        North Carolina         0.5       44.0             3.0   \n",
       "51              Virginia         0.2       30.0             4.0   \n",
       "52              Virginia         0.2       34.0             4.0   \n",
       "53              Virginia         0.2       34.0             4.0   \n",
       "54              Virginia         0.4       34.0             4.0   \n",
       "55                  Ohio         0.3       38.0             6.4   \n",
       "56                  Ohio         0.4       45.0             6.4   \n",
       "57                  Ohio         0.9       47.0             6.4   \n",
       "58                  Ohio         0.3       27.0             6.4   \n",
       "59               Georgia         0.2       47.0             2.2   \n",
       "60              Michigan         0.7       47.0             4.7   \n",
       "61               Indiana         0.3       25.0             2.7   \n",
       "62               Indiana         0.9       41.0             2.7   \n",
       "63                Nevada         0.3       41.0             6.3   \n",
       "64                Nevada         0.7       49.0             6.3   \n",
       "65                Nevada         0.3       39.0             6.3   \n",
       "66                Nevada         0.3       31.0             6.3   \n",
       "67               Florida         0.2       43.0             6.2   \n",
       "68               Florida         0.9       37.0             6.2   \n",
       "69               Florida         0.5       50.0             6.2   \n",
       "70               Florida         0.3       45.0             6.2   \n",
       "71               Florida         0.3       52.0             6.2   \n",
       "72               Florida         0.4       41.0             6.2   \n",
       "73                Hawaii         0.3       40.0             2.1   \n",
       "74            New Jersey         0.3       60.0            16.8   \n",
       "75            New Jersey         0.3       52.0            16.8   \n",
       "76              Missouri         0.5       43.0             3.4   \n",
       "77              Missouri         0.3       42.0             3.4   \n",
       "78                Kansas         0.2       43.0             2.3   \n",
       "79                Kansas         0.4       29.0             2.3   \n",
       "80              Kentucky         0.3       30.0             2.0   \n",
       "81              Nebraska         0.3       26.0             2.8   \n",
       "82              Nebraska         0.5       33.0             2.8   \n",
       "83             Wisconsin         0.3       42.0             3.5   \n",
       "84             Tennessee         0.7       65.0             2.2   \n",
       "85             Tennessee         0.7       65.0             2.2   \n",
       "86             Tennessee         0.7       42.0             2.2   \n",
       "87             Minnesota         0.4       48.0             2.3   \n",
       "88             Minnesota         0.3       47.0             2.3   \n",
       "89              Oklahoma         0.7       35.0             1.7   \n",
       "90              Oklahoma         0.4       34.0             1.7   \n",
       "91          Pennsylvania         1.6       52.0            12.2   \n",
       "92          Pennsylvania         0.3       45.0            12.2   \n",
       "93                Oregon         0.7       52.0             3.8   \n",
       "94            Washington         0.8       51.0             3.6   \n",
       "95            Washington         0.2       30.0             3.6   \n",
       "96  District of Columbia         0.7       50.0             3.3   \n",
       "\n",
       "    italian_rest_perc  \n",
       "0               0.015  \n",
       "1               0.038  \n",
       "2               0.011  \n",
       "3               0.135  \n",
       "4               0.035  \n",
       "5               0.021  \n",
       "6               0.028  \n",
       "7               0.050  \n",
       "8               0.024  \n",
       "9               0.084  \n",
       "10              0.032  \n",
       "11              0.033  \n",
       "12              0.065  \n",
       "13              0.054  \n",
       "14              0.032  \n",
       "15              0.008  \n",
       "16              0.027  \n",
       "17              0.053  \n",
       "18              0.022  \n",
       "19              0.078  \n",
       "20              0.049  \n",
       "21              0.058  \n",
       "22              0.069  \n",
       "23              0.063  \n",
       "24              0.012  \n",
       "25              0.055  \n",
       "26              0.019  \n",
       "27              0.023  \n",
       "28              0.047  \n",
       "29              0.043  \n",
       "30              0.070  \n",
       "31              0.067  \n",
       "32              0.094  \n",
       "33              0.086  \n",
       "34              0.067  \n",
       "35              0.067  \n",
       "36              0.068  \n",
       "37              0.068  \n",
       "38              0.078  \n",
       "39              0.080  \n",
       "40              0.061  \n",
       "41              0.041  \n",
       "42              0.028  \n",
       "43              0.035  \n",
       "44              0.045  \n",
       "45              0.081  \n",
       "46              0.083  \n",
       "47              0.058  \n",
       "48              0.060  \n",
       "49              0.062  \n",
       "50              0.068  \n",
       "51              0.037  \n",
       "52              0.044  \n",
       "53              0.084  \n",
       "54              0.079  \n",
       "55              0.051  \n",
       "56              0.074  \n",
       "57              0.073  \n",
       "58              0.121  \n",
       "59              0.073  \n",
       "60              0.071  \n",
       "61              0.065  \n",
       "62              0.061  \n",
       "63              0.040  \n",
       "64              0.070  \n",
       "65              0.087  \n",
       "66              0.058  \n",
       "67              0.014  \n",
       "68              0.087  \n",
       "69              0.058  \n",
       "70              0.057  \n",
       "71              0.077  \n",
       "72              0.051  \n",
       "73              0.041  \n",
       "74              0.034  \n",
       "75              0.027  \n",
       "76              0.098  \n",
       "77              0.121  \n",
       "78              0.098  \n",
       "79              0.053  \n",
       "80              0.061  \n",
       "81              0.054  \n",
       "82              0.092  \n",
       "83              0.056  \n",
       "84              0.035  \n",
       "85              0.035  \n",
       "86              0.077  \n",
       "87              0.056  \n",
       "88              0.055  \n",
       "89              0.066  \n",
       "90              0.052  \n",
       "91              0.068  \n",
       "92              0.103  \n",
       "93              0.055  \n",
       "94              0.061  \n",
       "95              0.057  \n",
       "96              0.070  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cities = pd.merge(df_cities, df_italian_origin, on='State')\n",
    "df_cities['italian_rest_perc'] = round(df_cities['Italian restaurants']/df_cities['Total restaurants'],3)\n",
    "df_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Total restaurants</th>\n",
       "      <th>Italian restaurants</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Diversity</th>\n",
       "      <th>Italian origin</th>\n",
       "      <th>italian_rest_perc</th>\n",
       "      <th>italian_rest_per_m_pop</th>\n",
       "      <th>total_rest_per_m_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>1451</td>\n",
       "      <td>22</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>557</td>\n",
       "      <td>21</td>\n",
       "      <td>California</td>\n",
       "      <td>0.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>1008</td>\n",
       "      <td>11</td>\n",
       "      <td>California</td>\n",
       "      <td>0.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>1253</td>\n",
       "      <td>169</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.84</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>821</td>\n",
       "      <td>29</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.14</td>\n",
       "      <td>4.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City  Total restaurants  Italian restaurants       State  \\\n",
       "0      Anaheim               1451                   22  California   \n",
       "1  Bakersfield                557                   21  California   \n",
       "2  Chula Vista               1008                   11  California   \n",
       "3      Fremont               1253                  169  California   \n",
       "4     Glendale                821                   29  California   \n",
       "\n",
       "   Population  Diversity  Italian origin  italian_rest_perc  \\\n",
       "0         0.4       48.0             4.0              0.015   \n",
       "1         0.4       25.0             4.0              0.038   \n",
       "2         0.3       42.0             4.0              0.011   \n",
       "3         0.2       44.0             4.0              0.135   \n",
       "4         0.2       37.0             4.0              0.035   \n",
       "\n",
       "   italian_rest_per_m_pop  total_rest_per_m_pop  \n",
       "0                    0.06                  3.63  \n",
       "1                    0.05                  1.39  \n",
       "2                    0.04                  3.36  \n",
       "3                    0.84                  6.26  \n",
       "4                    0.14                  4.11  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cities['italian_rest_per_m_pop']=round(df_cities['Italian restaurants']/(df_cities['Population']*1000),2)\n",
    "df_cities['total_rest_per_m_pop']=round(df_cities['Total restaurants']/(df_cities['Population']*1000),2)\n",
    "\n",
    "df_cities.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Italian restaurants per 1000 pop.')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar chart\n",
    "#ax3 = df_cities.sort_values('total_rest_per_m_pop', ascending = False).plot.bar(x = 'City',y =['total_rest_per_m_pop','italian_rest_per_m_pop'],rot=0, figsize = (15,5), sort_columns = True)\n",
    "\n",
    "#scatter chart\n",
    "#ax3 = df_cities.plot.scatter(x='total_rest_per_m_pop',y='italian_rest_per_m_pop',c='DarkBlue',figsize = (15,5))\n",
    "\n",
    "df_cities['bubble_size']=df_cities['Population']*50\n",
    "plt.figure(figsize=(15, 15))\n",
    "ax4 = plt.scatter(x='total_rest_per_m_pop',\n",
    "                      y='italian_rest_per_m_pop',s='bubble_size',\n",
    "                      c='Green', data=df_cities)\n",
    "plt.xlabel(\"Total restaurants per 1000 pop.\")\n",
    "plt.ylabel(\"Italian restaurants per 1000 pop.\")\n",
    "\n",
    "#df_cities[['City','total_rest_per_m_pop','italian_rest_per_m_pop','Population']].sort_values(by ='italian_rest_per_m_pop', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Italian restaurants perc. of total')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cities['bubble_size']=df_cities['Population']*50\n",
    "plt.figure(figsize=(15, 15))\n",
    "ax4 = plt.scatter(x='total_rest_per_m_pop',\n",
    "                      y='italian_rest_perc',s='bubble_size',\n",
    "                      c='Purple', data=df_cities)\n",
    "plt.xlabel(\"Total restaurants per 1000 pop.\")\n",
    "plt.ylabel(\"Italian restaurants perc. of total\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The more restaurants per pop, the lower the percentage of Italian restaurants.  \n",
    "Higher population, less Italian restaurants. Probably because more types of food are available. Let's see if the number of cuisines available is the driver. And also let's see if the more population means more variety of food always."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Italian restaurants perc. of total')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cities['bubble_size']=df_cities['Population']*50\n",
    "plt.figure(figsize=(15, 15))\n",
    "ax4 = plt.scatter(x='Diversity',\n",
    "                      y='italian_rest_perc',s='bubble_size',\n",
    "                      c='Brown', data=df_cities)\n",
    "plt.xlabel(\"Diversity\")\n",
    "plt.ylabel(\"Italian restaurants perc. of total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>italian_rest_perc</th>\n",
       "      <th>total_rest_per_m_pop</th>\n",
       "      <th>Italian origin</th>\n",
       "      <th>Diversity</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Santa Ana</td>\n",
       "      <td>California</td>\n",
       "      <td>0.008</td>\n",
       "      <td>4.71</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>California</td>\n",
       "      <td>0.011</td>\n",
       "      <td>3.36</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Garland</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.012</td>\n",
       "      <td>7.24</td>\n",
       "      <td>1.9</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Hialeah</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.014</td>\n",
       "      <td>5.34</td>\n",
       "      <td>6.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>California</td>\n",
       "      <td>0.015</td>\n",
       "      <td>3.63</td>\n",
       "      <td>4.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Irving</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.019</td>\n",
       "      <td>6.28</td>\n",
       "      <td>1.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Irvine</td>\n",
       "      <td>California</td>\n",
       "      <td>0.021</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.022</td>\n",
       "      <td>2.78</td>\n",
       "      <td>1.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Laredo</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.023</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>California</td>\n",
       "      <td>0.024</td>\n",
       "      <td>3.80</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stockton</td>\n",
       "      <td>California</td>\n",
       "      <td>0.027</td>\n",
       "      <td>2.13</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Newark</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.027</td>\n",
       "      <td>9.46</td>\n",
       "      <td>16.8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.028</td>\n",
       "      <td>2.63</td>\n",
       "      <td>4.4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Long Beach</td>\n",
       "      <td>California</td>\n",
       "      <td>0.028</td>\n",
       "      <td>3.19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>California</td>\n",
       "      <td>0.032</td>\n",
       "      <td>1.73</td>\n",
       "      <td>4.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>California</td>\n",
       "      <td>0.032</td>\n",
       "      <td>3.34</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>California</td>\n",
       "      <td>0.033</td>\n",
       "      <td>2.74</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Jersey City</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.034</td>\n",
       "      <td>10.35</td>\n",
       "      <td>16.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.035</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.2</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.035</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.2</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.035</td>\n",
       "      <td>2.74</td>\n",
       "      <td>4.4</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>California</td>\n",
       "      <td>0.035</td>\n",
       "      <td>4.11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Chesapeake</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.037</td>\n",
       "      <td>2.44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>California</td>\n",
       "      <td>0.038</td>\n",
       "      <td>1.39</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.040</td>\n",
       "      <td>3.40</td>\n",
       "      <td>6.3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.041</td>\n",
       "      <td>2.29</td>\n",
       "      <td>4.4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Honolulu</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>0.041</td>\n",
       "      <td>5.08</td>\n",
       "      <td>2.1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Plano</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.043</td>\n",
       "      <td>3.87</td>\n",
       "      <td>1.9</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Norfolk</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.044</td>\n",
       "      <td>2.58</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.045</td>\n",
       "      <td>2.13</td>\n",
       "      <td>4.4</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.9</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.98</td>\n",
       "      <td>4.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.051</td>\n",
       "      <td>3.17</td>\n",
       "      <td>6.4</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Tampa</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.051</td>\n",
       "      <td>3.95</td>\n",
       "      <td>6.2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Tulsa</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.052</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.7</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Wichita</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.053</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.053</td>\n",
       "      <td>1.38</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.054</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>0.054</td>\n",
       "      <td>3.46</td>\n",
       "      <td>4.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.055</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Portland</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2.83</td>\n",
       "      <td>3.8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Madison</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.056</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3.5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0.056</td>\n",
       "      <td>3.08</td>\n",
       "      <td>2.3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.057</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.6</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Orlando</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.057</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.9</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Charlotte</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.76</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Reno</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6.3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.058</td>\n",
       "      <td>4.30</td>\n",
       "      <td>6.2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Durham</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.060</td>\n",
       "      <td>2.04</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.66</td>\n",
       "      <td>13.5</td>\n",
       "      <td>61.0</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.061</td>\n",
       "      <td>1.34</td>\n",
       "      <td>2.7</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Lexington</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>0.061</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.061</td>\n",
       "      <td>2.92</td>\n",
       "      <td>3.6</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Greensboro</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.062</td>\n",
       "      <td>2.27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.063</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.9</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.065</td>\n",
       "      <td>1.39</td>\n",
       "      <td>2.7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>California</td>\n",
       "      <td>0.065</td>\n",
       "      <td>1.68</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.7</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>0.067</td>\n",
       "      <td>1.19</td>\n",
       "      <td>6.2</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.067</td>\n",
       "      <td>2.53</td>\n",
       "      <td>5.1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>0.067</td>\n",
       "      <td>5.06</td>\n",
       "      <td>6.2</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>0.068</td>\n",
       "      <td>1.54</td>\n",
       "      <td>12.2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Raleigh</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0.068</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.068</td>\n",
       "      <td>2.42</td>\n",
       "      <td>4.8</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0.068</td>\n",
       "      <td>2.66</td>\n",
       "      <td>4.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>El Paso</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.069</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>San Antonio</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.070</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.9</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Washington</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>0.070</td>\n",
       "      <td>2.84</td>\n",
       "      <td>3.3</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.070</td>\n",
       "      <td>3.28</td>\n",
       "      <td>6.3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Detroit</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>0.071</td>\n",
       "      <td>1.38</td>\n",
       "      <td>4.7</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.073</td>\n",
       "      <td>1.43</td>\n",
       "      <td>6.4</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0.073</td>\n",
       "      <td>6.42</td>\n",
       "      <td>2.2</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.074</td>\n",
       "      <td>2.23</td>\n",
       "      <td>6.4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Nashville</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.077</td>\n",
       "      <td>1.66</td>\n",
       "      <td>2.2</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.077</td>\n",
       "      <td>5.12</td>\n",
       "      <td>6.2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Austin</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.078</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Boston</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>0.078</td>\n",
       "      <td>2.73</td>\n",
       "      <td>13.9</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Virginia Beach</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.079</td>\n",
       "      <td>1.94</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.080</td>\n",
       "      <td>2.05</td>\n",
       "      <td>13.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.95</td>\n",
       "      <td>4.4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.083</td>\n",
       "      <td>3.78</td>\n",
       "      <td>4.4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.084</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>California</td>\n",
       "      <td>0.084</td>\n",
       "      <td>9.05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.086</td>\n",
       "      <td>2.41</td>\n",
       "      <td>5.1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>Florida</td>\n",
       "      <td>0.087</td>\n",
       "      <td>1.37</td>\n",
       "      <td>6.2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>North Las Vegas</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.087</td>\n",
       "      <td>3.53</td>\n",
       "      <td>6.3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Omaha</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0.092</td>\n",
       "      <td>1.19</td>\n",
       "      <td>2.8</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.094</td>\n",
       "      <td>1.76</td>\n",
       "      <td>5.1</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.098</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.4</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>0.098</td>\n",
       "      <td>4.86</td>\n",
       "      <td>2.3</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>0.103</td>\n",
       "      <td>4.16</td>\n",
       "      <td>12.2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.121</td>\n",
       "      <td>1.77</td>\n",
       "      <td>6.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.121</td>\n",
       "      <td>3.98</td>\n",
       "      <td>3.4</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>California</td>\n",
       "      <td>0.135</td>\n",
       "      <td>6.26</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City                 State  italian_rest_perc  \\\n",
       "15         Santa Ana            California              0.008   \n",
       "2        Chula Vista            California              0.011   \n",
       "24           Garland                 Texas              0.012   \n",
       "67           Hialeah               Florida              0.014   \n",
       "0            Anaheim            California              0.015   \n",
       "26            Irving                 Texas              0.019   \n",
       "5             Irvine            California              0.021   \n",
       "18         Arlington                 Texas              0.022   \n",
       "27            Laredo                 Texas              0.023   \n",
       "8            Oakland            California              0.024   \n",
       "16          Stockton            California              0.027   \n",
       "75            Newark            New Jersey              0.027   \n",
       "42           Gilbert               Arizona              0.028   \n",
       "6         Long Beach            California              0.028   \n",
       "14          San Jose            California              0.032   \n",
       "10         Riverside            California              0.032   \n",
       "11        Sacramento            California              0.033   \n",
       "74       Jersey City            New Jersey              0.034   \n",
       "84           Memphis             Tennessee              0.035   \n",
       "85           Memphis             Tennessee              0.035   \n",
       "43          Glendale               Arizona              0.035   \n",
       "4           Glendale            California              0.035   \n",
       "51        Chesapeake              Virginia              0.037   \n",
       "1        Bakersfield            California              0.038   \n",
       "63         Henderson                Nevada              0.040   \n",
       "41          Chandler               Arizona              0.041   \n",
       "73          Honolulu                Hawaii              0.041   \n",
       "29             Plano                 Texas              0.043   \n",
       "52           Norfolk              Virginia              0.044   \n",
       "44              Mesa               Arizona              0.045   \n",
       "28           Lubbock                 Texas              0.047   \n",
       "20    Corpus Christi                 Texas              0.049   \n",
       "7        Los Angeles            California              0.050   \n",
       "55        Cincinnati                  Ohio              0.051   \n",
       "72             Tampa               Florida              0.051   \n",
       "90             Tulsa              Oklahoma              0.052   \n",
       "79           Wichita                Kansas              0.053   \n",
       "17         Anchorage                Alaska              0.053   \n",
       "81           Lincoln              Nebraska              0.054   \n",
       "13     San Francisco            California              0.054   \n",
       "25           Houston                 Texas              0.055   \n",
       "88        Saint Paul             Minnesota              0.055   \n",
       "93          Portland                Oregon              0.055   \n",
       "83           Madison             Wisconsin              0.056   \n",
       "87       Minneapolis             Minnesota              0.056   \n",
       "95           Spokane            Washington              0.057   \n",
       "70           Orlando               Florida              0.057   \n",
       "21            Dallas                 Texas              0.058   \n",
       "47         Charlotte        North Carolina              0.058   \n",
       "66              Reno                Nevada              0.058   \n",
       "69             Miami               Florida              0.058   \n",
       "48            Durham        North Carolina              0.060   \n",
       "40     New York City              New York              0.061   \n",
       "62      Indianapolis               Indiana              0.061   \n",
       "80         Lexington              Kentucky              0.061   \n",
       "94           Seattle            Washington              0.061   \n",
       "49        Greensboro        North Carolina              0.062   \n",
       "23        Fort Worth                 Texas              0.063   \n",
       "61        Fort Wayne               Indiana              0.065   \n",
       "12         San Diego            California              0.065   \n",
       "89     Oklahoma City              Oklahoma              0.066   \n",
       "35           Chicago              Illinois              0.067   \n",
       "31            Aurora              Colorado              0.067   \n",
       "34            Aurora              Illinois              0.067   \n",
       "91      Philadelphia          Pennsylvania              0.068   \n",
       "50           Raleigh        North Carolina              0.068   \n",
       "37       New Orleans             Louisiana              0.068   \n",
       "36       Baton Rouge             Louisiana              0.068   \n",
       "22           El Paso                 Texas              0.069   \n",
       "30       San Antonio                 Texas              0.070   \n",
       "96        Washington  District of Columbia              0.070   \n",
       "64         Las Vegas                Nevada              0.070   \n",
       "60           Detroit              Michigan              0.071   \n",
       "57          Columbus                  Ohio              0.073   \n",
       "59          Columbus               Georgia              0.073   \n",
       "56         Cleveland                  Ohio              0.074   \n",
       "86         Nashville             Tennessee              0.077   \n",
       "71    St. Petersburg               Florida              0.077   \n",
       "19            Austin                 Texas              0.078   \n",
       "38            Boston         Massachusetts              0.078   \n",
       "54    Virginia Beach              Virginia              0.079   \n",
       "39           Buffalo              New York              0.080   \n",
       "45           Phoenix               Arizona              0.081   \n",
       "46        Scottsdale               Arizona              0.083   \n",
       "53          Richmond              Virginia              0.084   \n",
       "9           Richmond            California              0.084   \n",
       "33            Denver              Colorado              0.086   \n",
       "68      Jacksonville               Florida              0.087   \n",
       "65   North Las Vegas                Nevada              0.087   \n",
       "82             Omaha              Nebraska              0.092   \n",
       "32  Colorado Springs              Colorado              0.094   \n",
       "76       Kansas City              Missouri              0.098   \n",
       "78       Kansas City                Kansas              0.098   \n",
       "92        Pittsburgh          Pennsylvania              0.103   \n",
       "58            Toledo                  Ohio              0.121   \n",
       "77         St. Louis              Missouri              0.121   \n",
       "3            Fremont            California              0.135   \n",
       "\n",
       "    total_rest_per_m_pop  Italian origin  Diversity  Population  \n",
       "15                  4.71             4.0       44.0         0.3  \n",
       "2                   3.36             4.0       42.0         0.3  \n",
       "24                  7.24             1.9       44.0         0.2  \n",
       "67                  5.34             6.2       43.0         0.2  \n",
       "0                   3.63             4.0       48.0         0.4  \n",
       "26                  6.28             1.9       46.0         0.2  \n",
       "5                   4.01             4.0       41.0         0.3  \n",
       "18                  2.78             1.9       46.0         0.4  \n",
       "27                  1.14             1.9       16.0         0.3  \n",
       "8                   3.80             4.0       52.0         0.4  \n",
       "16                  2.13             4.0       28.0         0.3  \n",
       "75                  9.46            16.8       52.0         0.3  \n",
       "42                  2.63             4.4       41.0         0.3  \n",
       "6                   3.19             4.0       46.0         0.5  \n",
       "14                  1.73             4.0       46.0         1.0  \n",
       "10                  3.34             4.0       37.0         0.3  \n",
       "11                  2.74             4.0       42.0         0.5  \n",
       "74                 10.35            16.8       60.0         0.3  \n",
       "84                  2.31             2.2       65.0         0.7  \n",
       "85                  2.31             2.2       65.0         0.7  \n",
       "43                  2.74             4.4       37.0         0.3  \n",
       "4                   4.11             4.0       37.0         0.2  \n",
       "51                  2.44             4.0       30.0         0.2  \n",
       "1                   1.39             4.0       25.0         0.4  \n",
       "63                  3.40             6.3       41.0         0.3  \n",
       "41                  2.29             4.4       41.0         0.3  \n",
       "73                  5.08             2.1       40.0         0.3  \n",
       "29                  3.87             1.9       45.0         0.3  \n",
       "52                  2.58             4.0       34.0         0.2  \n",
       "44                  2.13             4.4       43.0         0.5  \n",
       "28                  1.20             1.9       22.0         0.3  \n",
       "20                  1.71             1.9       28.0         0.3  \n",
       "7                   0.98             4.0       53.0         4.0  \n",
       "55                  3.17             6.4       38.0         0.3  \n",
       "72                  3.95             6.2       41.0         0.4  \n",
       "90                  1.70             1.7       34.0         0.4  \n",
       "79                  1.23             2.3       29.0         0.4  \n",
       "17                  1.38             3.3       26.0         0.3  \n",
       "81                  1.42             2.8       26.0         0.3  \n",
       "13                  3.46             4.0       54.0         0.9  \n",
       "25                  1.29             1.9       48.0         2.3  \n",
       "88                  2.62             2.3       47.0         0.3  \n",
       "93                  2.83             3.8       52.0         0.7  \n",
       "83                  2.03             3.5       42.0         0.3  \n",
       "87                  3.08             2.3       48.0         0.4  \n",
       "95                  2.00             3.6       30.0         0.2  \n",
       "70                  6.02             6.2       45.0         0.3  \n",
       "21                  1.70             1.9       51.0         1.3  \n",
       "47                  1.76             3.0       42.0         0.9  \n",
       "66                  1.90             6.3       31.0         0.3  \n",
       "69                  4.30             6.2       50.0         0.5  \n",
       "48                  2.04             3.0       40.0         0.3  \n",
       "40                  0.66            13.5       61.0         8.3  \n",
       "62                  1.34             2.7       41.0         0.9  \n",
       "80                  1.87             2.0       30.0         0.3  \n",
       "94                  2.92             3.6       51.0         0.8  \n",
       "49                  2.27             3.0       32.0         0.3  \n",
       "23                  1.11             1.9       38.0         0.9  \n",
       "61                  1.39             2.7       25.0         0.3  \n",
       "12                  1.68             4.0       45.0         1.4  \n",
       "89                  1.31             1.7       35.0         0.7  \n",
       "35                  1.19             6.2       57.0         2.7  \n",
       "31                  2.53             5.1       47.0         0.4  \n",
       "34                  5.06             6.2       47.0         0.2  \n",
       "91                  1.54            12.2       52.0         1.6  \n",
       "50                  1.75             3.0       44.0         0.5  \n",
       "37                  2.42             4.8       45.0         0.4  \n",
       "36                  2.66             4.8       25.0         0.2  \n",
       "22                  1.12             1.9       25.0         0.7  \n",
       "30                  1.06             1.9       44.0         1.5  \n",
       "96                  2.84             3.3       50.0         0.7  \n",
       "64                  3.28             6.3       49.0         0.7  \n",
       "60                  1.38             4.7       47.0         0.7  \n",
       "57                  1.43             6.4       47.0         0.9  \n",
       "59                  6.42             2.2       47.0         0.2  \n",
       "56                  2.23             6.4       45.0         0.4  \n",
       "86                  1.66             2.2       42.0         0.7  \n",
       "71                  5.12             6.2       52.0         0.3  \n",
       "19                  1.64             1.9       50.0         1.0  \n",
       "38                  2.73            13.9       52.0         0.7  \n",
       "54                  1.94             4.0       34.0         0.4  \n",
       "39                  2.05            13.5       36.0         0.3  \n",
       "45                  0.95             4.4       45.0         1.7  \n",
       "46                  3.78             4.4       45.0         0.3  \n",
       "53                  4.53             4.0       34.0         0.2  \n",
       "9                   9.05             4.0       34.0         0.1  \n",
       "33                  2.41             5.1       49.0         0.7  \n",
       "68                  1.37             6.2       37.0         0.9  \n",
       "65                  3.53             6.3       39.0         0.3  \n",
       "82                  1.19             2.8       33.0         0.5  \n",
       "32                  1.76             5.1       36.0         0.5  \n",
       "76                  1.94             3.4       43.0         0.5  \n",
       "78                  4.86             2.3       43.0         0.2  \n",
       "92                  4.16            12.2       45.0         0.3  \n",
       "58                  1.77             6.4       27.0         0.3  \n",
       "77                  3.98             3.4       42.0         0.3  \n",
       "3                   6.26             4.0       44.0         0.2  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cities[['City','State','italian_rest_perc','total_rest_per_m_pop','Italian origin','Diversity','Population']].sort_values(by=['italian_rest_perc','total_rest_per_m_pop'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This concludes the Data section of this project. In the next sections we will discuss and further analyse the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>italian_rest_perc</th>\n",
       "      <th>Italian origin</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>0.015</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>0.038</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>0.011</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fremont</td>\n",
       "      <td>0.135</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>0.035</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Irvine</td>\n",
       "      <td>0.021</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Long Beach</td>\n",
       "      <td>0.028</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>0.050</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>0.024</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>0.084</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>0.032</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>0.033</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>0.065</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>0.054</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>0.032</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Santa Ana</td>\n",
       "      <td>0.008</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stockton</td>\n",
       "      <td>0.027</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>0.053</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>0.022</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Austin</td>\n",
       "      <td>0.078</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>El Paso</td>\n",
       "      <td>0.069</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>0.063</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Garland</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Houston</td>\n",
       "      <td>0.055</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Irving</td>\n",
       "      <td>0.019</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Laredo</td>\n",
       "      <td>0.023</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Plano</td>\n",
       "      <td>0.043</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>San Antonio</td>\n",
       "      <td>0.070</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>0.067</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>0.094</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Denver</td>\n",
       "      <td>0.086</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>0.067</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>0.067</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>0.068</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>New Orleans</td>\n",
       "      <td>0.068</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Boston</td>\n",
       "      <td>0.078</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Buffalo</td>\n",
       "      <td>0.080</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>New York City</td>\n",
       "      <td>0.061</td>\n",
       "      <td>13.5</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Chandler</td>\n",
       "      <td>0.041</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Gilbert</td>\n",
       "      <td>0.028</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>0.035</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Mesa</td>\n",
       "      <td>0.045</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>0.081</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>0.083</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Charlotte</td>\n",
       "      <td>0.058</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Durham</td>\n",
       "      <td>0.060</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Greensboro</td>\n",
       "      <td>0.062</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Raleigh</td>\n",
       "      <td>0.068</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Chesapeake</td>\n",
       "      <td>0.037</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Norfolk</td>\n",
       "      <td>0.044</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>0.084</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Virginia Beach</td>\n",
       "      <td>0.079</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>0.051</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>0.074</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>0.073</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Toledo</td>\n",
       "      <td>0.121</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Columbus</td>\n",
       "      <td>0.073</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Detroit</td>\n",
       "      <td>0.071</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>0.065</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>0.061</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>0.040</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>0.070</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>North Las Vegas</td>\n",
       "      <td>0.087</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Reno</td>\n",
       "      <td>0.058</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Hialeah</td>\n",
       "      <td>0.014</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>0.087</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Miami</td>\n",
       "      <td>0.058</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Orlando</td>\n",
       "      <td>0.057</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>0.077</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Tampa</td>\n",
       "      <td>0.051</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Honolulu</td>\n",
       "      <td>0.041</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Jersey City</td>\n",
       "      <td>0.034</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Newark</td>\n",
       "      <td>0.027</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>0.098</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>0.121</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Kansas City</td>\n",
       "      <td>0.098</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Wichita</td>\n",
       "      <td>0.053</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Lexington</td>\n",
       "      <td>0.061</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>0.054</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Omaha</td>\n",
       "      <td>0.092</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Madison</td>\n",
       "      <td>0.056</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>0.035</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Memphis</td>\n",
       "      <td>0.035</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Nashville</td>\n",
       "      <td>0.077</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>0.056</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Tulsa</td>\n",
       "      <td>0.052</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>0.068</td>\n",
       "      <td>12.2</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>0.103</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Portland</td>\n",
       "      <td>0.055</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>0.061</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>0.057</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Washington</td>\n",
       "      <td>0.070</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  italian_rest_perc  Italian origin  Population\n",
       "0            Anaheim              0.015             4.0         0.4\n",
       "1        Bakersfield              0.038             4.0         0.4\n",
       "2        Chula Vista              0.011             4.0         0.3\n",
       "3            Fremont              0.135             4.0         0.2\n",
       "4           Glendale              0.035             4.0         0.2\n",
       "5             Irvine              0.021             4.0         0.3\n",
       "6         Long Beach              0.028             4.0         0.5\n",
       "7        Los Angeles              0.050             4.0         4.0\n",
       "8            Oakland              0.024             4.0         0.4\n",
       "9           Richmond              0.084             4.0         0.1\n",
       "10         Riverside              0.032             4.0         0.3\n",
       "11        Sacramento              0.033             4.0         0.5\n",
       "12         San Diego              0.065             4.0         1.4\n",
       "13     San Francisco              0.054             4.0         0.9\n",
       "14          San Jose              0.032             4.0         1.0\n",
       "15         Santa Ana              0.008             4.0         0.3\n",
       "16          Stockton              0.027             4.0         0.3\n",
       "17         Anchorage              0.053             3.3         0.3\n",
       "18         Arlington              0.022             1.9         0.4\n",
       "19            Austin              0.078             1.9         1.0\n",
       "20    Corpus Christi              0.049             1.9         0.3\n",
       "21            Dallas              0.058             1.9         1.3\n",
       "22           El Paso              0.069             1.9         0.7\n",
       "23        Fort Worth              0.063             1.9         0.9\n",
       "24           Garland              0.012             1.9         0.2\n",
       "25           Houston              0.055             1.9         2.3\n",
       "26            Irving              0.019             1.9         0.2\n",
       "27            Laredo              0.023             1.9         0.3\n",
       "28           Lubbock              0.047             1.9         0.3\n",
       "29             Plano              0.043             1.9         0.3\n",
       "30       San Antonio              0.070             1.9         1.5\n",
       "31            Aurora              0.067             5.1         0.4\n",
       "32  Colorado Springs              0.094             5.1         0.5\n",
       "33            Denver              0.086             5.1         0.7\n",
       "34            Aurora              0.067             6.2         0.2\n",
       "35           Chicago              0.067             6.2         2.7\n",
       "36       Baton Rouge              0.068             4.8         0.2\n",
       "37       New Orleans              0.068             4.8         0.4\n",
       "38            Boston              0.078            13.9         0.7\n",
       "39           Buffalo              0.080            13.5         0.3\n",
       "40     New York City              0.061            13.5         8.3\n",
       "41          Chandler              0.041             4.4         0.3\n",
       "42           Gilbert              0.028             4.4         0.3\n",
       "43          Glendale              0.035             4.4         0.3\n",
       "44              Mesa              0.045             4.4         0.5\n",
       "45           Phoenix              0.081             4.4         1.7\n",
       "46        Scottsdale              0.083             4.4         0.3\n",
       "47         Charlotte              0.058             3.0         0.9\n",
       "48            Durham              0.060             3.0         0.3\n",
       "49        Greensboro              0.062             3.0         0.3\n",
       "50           Raleigh              0.068             3.0         0.5\n",
       "51        Chesapeake              0.037             4.0         0.2\n",
       "52           Norfolk              0.044             4.0         0.2\n",
       "53          Richmond              0.084             4.0         0.2\n",
       "54    Virginia Beach              0.079             4.0         0.4\n",
       "55        Cincinnati              0.051             6.4         0.3\n",
       "56         Cleveland              0.074             6.4         0.4\n",
       "57          Columbus              0.073             6.4         0.9\n",
       "58            Toledo              0.121             6.4         0.3\n",
       "59          Columbus              0.073             2.2         0.2\n",
       "60           Detroit              0.071             4.7         0.7\n",
       "61        Fort Wayne              0.065             2.7         0.3\n",
       "62      Indianapolis              0.061             2.7         0.9\n",
       "63         Henderson              0.040             6.3         0.3\n",
       "64         Las Vegas              0.070             6.3         0.7\n",
       "65   North Las Vegas              0.087             6.3         0.3\n",
       "66              Reno              0.058             6.3         0.3\n",
       "67           Hialeah              0.014             6.2         0.2\n",
       "68      Jacksonville              0.087             6.2         0.9\n",
       "69             Miami              0.058             6.2         0.5\n",
       "70           Orlando              0.057             6.2         0.3\n",
       "71    St. Petersburg              0.077             6.2         0.3\n",
       "72             Tampa              0.051             6.2         0.4\n",
       "73          Honolulu              0.041             2.1         0.3\n",
       "74       Jersey City              0.034            16.8         0.3\n",
       "75            Newark              0.027            16.8         0.3\n",
       "76       Kansas City              0.098             3.4         0.5\n",
       "77         St. Louis              0.121             3.4         0.3\n",
       "78       Kansas City              0.098             2.3         0.2\n",
       "79           Wichita              0.053             2.3         0.4\n",
       "80         Lexington              0.061             2.0         0.3\n",
       "81           Lincoln              0.054             2.8         0.3\n",
       "82             Omaha              0.092             2.8         0.5\n",
       "83           Madison              0.056             3.5         0.3\n",
       "84           Memphis              0.035             2.2         0.7\n",
       "85           Memphis              0.035             2.2         0.7\n",
       "86         Nashville              0.077             2.2         0.7\n",
       "87       Minneapolis              0.056             2.3         0.4\n",
       "88        Saint Paul              0.055             2.3         0.3\n",
       "89     Oklahoma City              0.066             1.7         0.7\n",
       "90             Tulsa              0.052             1.7         0.4\n",
       "91      Philadelphia              0.068            12.2         1.6\n",
       "92        Pittsburgh              0.103            12.2         0.3\n",
       "93          Portland              0.055             3.8         0.7\n",
       "94           Seattle              0.061             3.6         0.8\n",
       "95           Spokane              0.057             3.6         0.2\n",
       "96        Washington              0.070             3.3         0.7"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cities['bubble_size']=df_cities['Population']*100\n",
    "\n",
    "ax4 = plt.scatter(x='Italian origin',\n",
    "                      y='italian_rest_perc',s='bubble_size',\n",
    "                      c='Green', data=df_cities)\n",
    "plt.xlabel(\"Italian origin % of population\")\n",
    "plt.ylabel(\"Italian restaurants % of total\")\n",
    "df_cities[['City','italian_rest_perc','Italian origin','Population']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "xxxx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "The larger the city, there is more competition among different cuisines, so a good approach would be to look for opportunities in smaller cities that are lagging behind cities of similar size.\n",
    "\n",
    "Following this approach, a good recommendation would be to look at San Jose, since comparing it to other cities in California, it has less percentage of Italian restaurants.\n",
    "\n",
    "On the other hand, Philadelphia shows up as a city with potential. A large percentage of Italian origin population in the state could also build up on the same direction of investing in this city."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "\n",
    "\n",
    "\n",
    "#### Further research and caveats of this analysis\n",
    "\n",
    "##### Caveats\n",
    "- Foursquare data may be uneven in different cities even if we have chosen all cities in US  \n",
    "- Ethnic origin of population is at state level, not city level\n",
    "- The population in the Wikipedia table may refer to a different geography than the geography applied by Foursquare in the queries\n",
    "\n",
    "##### Further research\n",
    "- Look at the evolution in time  \n",
    "- Analyse the preferences of the consumers also in terms of assessment of the existing restaurants  \n",
    "- Differentiate different level of budget in the restaurants  \n",
    "- Look at composition of the population and income in the city\n",
    "- Zoom into the cities that have been selected to better understand the competition, both in geographical terms as well as other types of cuisines\n",
    "- Polinomial regression analysis: number or IT rest (y), number of rest, pop size, italian origin, average punctuation feedback"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
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