Segmenting and Clustering 3.ipynb

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    "## Assignment IBM\n",
    "### Week 3 Assignment of Applied Data Science Capstone\n",
    "### Task 3: Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Libraries"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geocoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the DataFrame from Part1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "[43.70976500000006, -79.36379132299999]"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_latlng(postal_code):\n",
    "    # initialize your variable to None\n",
    "    lat_lng_coords = None\n",
    "    # loop until you get the coordinates\n",
    "    while(lat_lng_coords is None):\n",
    "        g = geocoder.arcgis('{}, Toronto, Ontario'.format(postal_code))\n",
    "        lat_lng_coords = g.latlng\n",
    "    return lat_lng_coords\n",
    "    \n",
    "get_latlng('M4G')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieving Postal Code coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "postal_codes = df['Postalcode']    \n",
    "coords = [ get_latlng(postal_code) for postal_code in postal_codes.tolist() ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding columns Latitude and Longitude "
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_coords = pd.DataFrame(coords, columns=['Latitude', 'Longitude'])\n",
    "df['Latitude'] = df_coords['Latitude']\n",
    "df['Longitude'] = df_coords['Longitude']"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postalcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "      <td>43.811650</td>\n",
       "      <td>-79.195561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "      <td>43.785605</td>\n",
       "      <td>-79.158701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "      <td>43.765690</td>\n",
       "      <td>-79.175299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.768216</td>\n",
       "      <td>-79.217610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.769608</td>\n",
       "      <td>-79.239440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "  Postalcode      Borough                            Neighborhood   Latitude  \\\n",
       "0        M1B  Scarborough                          Rouge, Malvern  43.811650   \n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union  43.785605   \n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill  43.765690   \n",
       "3        M1G  Scarborough                                  Woburn  43.768216   \n",
       "4        M1H  Scarborough                               Cedarbrae  43.769608   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.195561  \n",
       "1 -79.158701  \n",
       "2 -79.175299  \n",
       "3 -79.217610  \n",
       "4 -79.239440  "
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     "execution_count": 6,
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   ],
   "source": [
    "df.head(5)"
   ]
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   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: conda [-h] [-V] command ...\n",
      "conda: error: unrecognized arguments: # uncomment this line if you haven't completed the Foursquare API lab\n",
      "usage: conda [-h] [-V] command ...\n",
      "conda: error: unrecognized arguments: # uncomment this line if you haven't completed the Foursquare API lab\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup as bs\n",
    "import re\n",
    "\n",
    "import numpy as np # library to handle data in a vectorized manner\n",
    "import pandas as pd # library for data analsysis\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import json # library to handle JSON files\n",
    "\n",
    "!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\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"
   ]
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   "cell_type": "code",
   "execution_count": 13,
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     "name": "stderr",
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     "text": [
      "C:\\Users\\Ali\\Anaconda3\\lib\\site-packages\\geopy\\geocoders\\osm.py:143: UserWarning: Using Nominatim with the default \"geopy/1.17.0\" `user_agent` is strongly discouraged, as it violates Nominatim's ToS https://operations.osmfoundation.org/policies/nominatim/ and may possibly cause 403 and 429 HTTP errors. Please specify a custom `user_agent` with `Nominatim(user_agent=\"my-application\")` or by overriding the default `user_agent`: `geopy.geocoders.options.default_user_agent = \"my-application\"`. In geopy 2.0 this will become an exception.\n",
      "  UserWarning\n"
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     "text": [
      "The geograpical coordinate of Toronto are 43.653963, -79.387207.\n"
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a992af82b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address = 'Toronto'\n",
    "\n",
    "geolocator = Nominatim()\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Toronto are {}, {}.'.format(latitude, longitude))\n",
    "\n",
    "# create map of New York using latitude and longitude values\n",
    "map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(df['Latitude'], df['Longitude'], df['Borough'], df['Neighborhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_toronto)  \n",
    "    \n",
    "map_toronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ali\\Anaconda3\\lib\\site-packages\\geopy\\geocoders\\osm.py:143: UserWarning: Using Nominatim with the default \"geopy/1.17.0\" `user_agent` is strongly discouraged, as it violates Nominatim's ToS https://operations.osmfoundation.org/policies/nominatim/ and may possibly cause 403 and 429 HTTP errors. Please specify a custom `user_agent` with `Nominatim(user_agent=\"my-application\")` or by overriding the default `user_agent`: `geopy.geocoders.options.default_user_agent = \"my-application\"`. In geopy 2.0 this will become an exception.\n",
      "  UserWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Scarborough are 43.773077, -79.257774.\n"
     ]
    }
   ],
   "source": [
    "scarborough_data = df[df['Borough'] == 'Scarborough'].reset_index(drop=True)\n",
    "address1 = 'Scarborough,Toronto'\n",
    "\n",
    "geolocator1 = Nominatim()\n",
    "location1 = geolocator1.geocode(address1)\n",
    "latitude1 = location1.latitude\n",
    "longitude1 = location1.longitude\n",
    "print('The geograpical coordinate of Scarborough are {}, {}.'.format(latitude1, longitude1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a992e38eb8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_scarb = folium.Map(location=[latitude1, longitude1], zoom_start=11)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, label in zip(scarborough_data['Latitude'], scarborough_data['Longitude'], scarborough_data['Neighborhood']):\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_scarb)  \n",
    "    \n",
    "map_scarb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of Rouge, Malvern are 43.81165000000004, -79.19556138899998.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_latitude = scarborough_data.loc[0, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = scarborough_data.loc[0, 'Longitude'] # neighborhood longitude value\n",
    "\n",
    "neighborhood_name = scarborough_data.loc[0, 'Neighborhood'] # neighborhood name\n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
    "                                                               neighborhood_latitude, \n",
    "                                                               neighborhood_longitude))\n",
    "\n",
    "LIMIT = 100\n",
    "radius = 500\n",
    "url = 'https://api.foursquare.com/v2/venues/explore?client_id={}&client_secret={}&ll={},{}&v={}&radius={}&limit={}'.format(CLIENT_ID, CLIENT_SECRET, latitude1, longitude1, VERSION, radius, LIMIT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disney store</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>43.775537</td>\n",
       "      <td>-79.256833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DAVIDsTEA</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>43.776613</td>\n",
       "      <td>-79.258516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>St. Andrews Fish &amp; Chips</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "      <td>43.771865</td>\n",
       "      <td>-79.252645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tommy Hilfiger Company Store</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>43.776015</td>\n",
       "      <td>-79.257369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SEPHORA</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>43.775592</td>\n",
       "      <td>-79.258242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hot Topic</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>43.775450</td>\n",
       "      <td>-79.257929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Coliseum Scarborough Cinemas</td>\n",
       "      <td>Movie Theater</td>\n",
       "      <td>43.775995</td>\n",
       "      <td>-79.255649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chipotle Mexican Grill</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>43.776410</td>\n",
       "      <td>-79.258069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scarborough Town Centre</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>43.775231</td>\n",
       "      <td>-79.257462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>American Eagle Outfitters</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>43.775908</td>\n",
       "      <td>-79.258352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tim Hortons</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>43.769936</td>\n",
       "      <td>-79.260368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>lululemon athletica</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>43.775186</td>\n",
       "      <td>-79.258047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name           categories        lat        lng\n",
       "0                   Disney store     Toy / Game Store  43.775537 -79.256833\n",
       "1                      DAVIDsTEA             Tea Room  43.776613 -79.258516\n",
       "2       St. Andrews Fish & Chips    Fish & Chips Shop  43.771865 -79.252645\n",
       "3   Tommy Hilfiger Company Store       Clothing Store  43.776015 -79.257369\n",
       "4                        SEPHORA       Cosmetics Shop  43.775592 -79.258242\n",
       "5                      Hot Topic       Clothing Store  43.775450 -79.257929\n",
       "6   Coliseum Scarborough Cinemas        Movie Theater  43.775995 -79.255649\n",
       "7         Chipotle Mexican Grill   Mexican Restaurant  43.776410 -79.258069\n",
       "8        Scarborough Town Centre        Shopping Mall  43.775231 -79.257462\n",
       "9      American Eagle Outfitters       Clothing Store  43.775908 -79.258352\n",
       "10                   Tim Hortons          Coffee Shop  43.769936 -79.260368\n",
       "11           lululemon athletica  Sporting Goods Shop  43.775186 -79.258047"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = requests.get(url).json()\n",
    "venues = results['response']['groups'][0]['items']\n",
    "    \n",
    "nearby_venues = json_normalize(venues) # flatten JSON\n",
    "\n",
    "# filter columns\n",
    "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
    "nearby_venues =nearby_venues.loc[:, filtered_columns]\n",
    "\n",
    "# filter the category for each row\n",
    "nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean columns\n",
    "nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
    "\n",
    "nearby_venues.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rouge, Malvern\n",
      "Highland Creek, Rouge Hill, Port Union\n",
      "Guildwood, Morningside, West Hill\n",
      "Woburn\n",
      "Cedarbrae\n",
      "Scarborough Village\n",
      "East Birchmount Park, Ionview, Kennedy Park\n",
      "Clairlea, Golden Mile, Oakridge\n",
      "Cliffcrest, Cliffside, Scarborough Village West\n",
      "Birch Cliff, Cliffside West\n",
      "Dorset Park, Scarborough Town Centre, Wexford Heights\n",
      "Maryvale, Wexford\n",
      "Agincourt\n",
      "Clarks Corners, Sullivan, Tam O'Shanter\n",
      "Agincourt North, L'Amoreaux East, Milliken, Steeles East\n",
      "L'Amoreaux West, Steeles West\n",
      "Upper Rouge\n"
     ]
    }
   ],
   "source": [
    "scarborough_venues = getNearbyVenues(names=scarborough_data['Neighborhood'],\n",
    "                                   latitudes=scarborough_data['Latitude'],\n",
    "                                   longitudes=scarborough_data['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    Neighborhood Latitude  \\\n",
      "Neighborhood                                                                \n",
      "Agincourt                                                              15   \n",
      "Agincourt North, L'Amoreaux East, Milliken, Ste...                      1   \n",
      "Birch Cliff, Cliffside West                                             6   \n",
      "Cedarbrae                                                               2   \n",
      "\n",
      "                                                    Neighborhood Longitude  \\\n",
      "Neighborhood                                                                 \n",
      "Agincourt                                                               15   \n",
      "Agincourt North, L'Amoreaux East, Milliken, Ste...                       1   \n",
      "Birch Cliff, Cliffside West                                              6   \n",
      "Cedarbrae                                                                2   \n",
      "\n",
      "                                                    Venue  Venue Latitude  \\\n",
      "Neighborhood                                                                \n",
      "Agincourt                                              15              15   \n",
      "Agincourt North, L'Amoreaux East, Milliken, Ste...      1               1   \n",
      "Birch Cliff, Cliffside West                             6               6   \n",
      "Cedarbrae                                               2               2   \n",
      "\n",
      "                                                    Venue Longitude  \\\n",
      "Neighborhood                                                          \n",
      "Agincourt                                                        15   \n",
      "Agincourt North, L'Amoreaux East, Milliken, Ste...                1   \n",
      "Birch Cliff, Cliffside West                                       6   \n",
      "Cedarbrae                                                         2   \n",
      "\n",
      "                                                    Venue Category  \n",
      "Neighborhood                                                        \n",
      "Agincourt                                                       15  \n",
      "Agincourt North, L'Amoreaux East, Milliken, Ste...               1  \n",
      "Birch Cliff, Cliffside West                                      6  \n",
      "Cedarbrae                                                        2  \n"
     ]
    }
   ],
   "source": [
    "scarborough_venues.head(3)\n",
    "print(scarborough_venues.groupby('Neighborhood').count()[:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encoding\n",
    "scarb_onehot = pd.get_dummies(scarborough_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "scarb_onehot['Neighborhood'] = scarborough_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [scarb_onehot.columns[-1]] + list(scarb_onehot.columns[:-1])\n",
    "scarb_onehot = scarb_onehot[fixed_columns]\n",
    "\n",
    "scarb_grouped = scarb_onehot.groupby('Neighborhood').mean().reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting top 10 venues per neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = scarb_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(scarb_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(scarb_grouped.iloc[ind, :], num_top_venues)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run k-means to Cluster  into 5 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 3, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarb_data = scarborough_data.drop(16)\n",
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "scarb_grouped_clustering = scarb_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(scarb_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "scarb_merged = scarb_data\n",
    "\n",
    "# add clustering labels\n",
    "scarb_merged['Cluster Labels'] = kmeans.labels_\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "scarb_merged = scarb_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a992dbb320>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i+x+(i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(scarb_merged['Latitude'], scarb_merged['Longitude'], scarb_merged['Neighborhood'], scarb_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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