Assignment 2 - Segmenting and Clustering Neighborhoods in Toronto - Part 2.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## Assignment 2 - Segmenting and Clustering Neighborhoods in Toronto - Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have built a dataframe of the postal code of each neighborhood along with the borough name and neighborhood name, in order to utilize the Foursquare location data, we need to get the latitude and the longitude coordinates of each neighborhood.\n",
    "\n",
    "In an older version of this course, we were leveraging the Google Maps Geocoding API to get the latitude and the longitude coordinates of each neighborhood. However, recently Google started charging for their API: http://geoawesomeness.com/developers-up-in-arms-over-google-maps-api-insane-price-hike/, so we will use the Geocoder Python package instead: https://geocoder.readthedocs.io/index.html.\n",
    "\n",
    "The problem with this Package is you have to be persistent sometimes in order to get the geographical coordinates of a given postal code. So you can make a call to get the latitude and longitude coordinates of a given postal code and the result would be None, and then make the call again and you would get the coordinates. So, in order to make sure that you get the coordinates for all of our neighborhoods, you can run a while loop for each postal code. Taking postal code M5G as an example, your code would look something like this:"
   ]
  },
  {
   "attachments": {
    "Screenshot%202019-04-19%20at%201.25.37%20PM.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screenshot%202019-04-19%20at%201.25.37%20PM.png](attachment:Screenshot%202019-04-19%20at%201.25.37%20PM.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that this package can be very unreliable, in case you are not able to get the geographical coordinates of the neighborhoods using the Geocoder package, here is a link to a csv file that has the geographical coordinates of each postal code: http://cocl.us/Geospatial_data\n",
    "\n",
    "Use the Geocoder package or the csv file to create the following dataframe:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/HZ3jNHNOEeiMwApe4i-fLg_f44f0f10ccfaf42fcbdba9813364e173_Screen-Shot-2018-06-18-at-7.18.16-PM.png?expiry=1555718400000&hmac=p3isop4LHOw_CMXIO0wZm4KFOACAt4FXI6kp-1gH2lw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the Latitude and Longitude of Canada Ontario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"import requests # library to handle requests\\nimport pandas as pd # library for data analsysis\\nimport numpy as np # library to handle data in a vectorized manner\\nimport random # library for random number generation\\n\\n!conda install -c conda-forge geopy --yes \\nfrom geopy.geocoders import Nominatim # module to convert an address into latitude and longitude values\\n\\n# libraries for displaying images\\nfrom IPython.display import Image \\nfrom IPython.core.display import HTML \\n    \\n# tranforming json file into a pandas dataframe library\\nfrom pandas.io.json import json_normalize\\n\\n!conda install -c conda-forge folium=0.5.0 --yes\\nimport folium # plotting library\\n\\nprint('Folium installed')\\nprint('Libraries imported.')\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"import requests # library to handle requests\n",
    "import pandas as pd # library for data analsysis\n",
    "import numpy as np # library to handle data in a vectorized manner\n",
    "import random # library for random number generation\n",
    "\n",
    "!conda install -c conda-forge geopy --yes \n",
    "from geopy.geocoders import Nominatim # module to convert an address into latitude and longitude values\n",
    "\n",
    "# libraries for displaying images\n",
    "from IPython.display import Image \n",
    "from IPython.core.display import HTML \n",
    "    \n",
    "# tranforming json file into a pandas dataframe library\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "!conda install -c conda-forge folium=0.5.0 --yes\n",
    "import folium # plotting library\n",
    "\n",
    "print('Folium installed')\n",
    "print('Libraries imported.')\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"import geocoder # import geocoder\\n\\n# initialize your variable to None\\nlat_lng_coords = None\\n\\n# loop until you get the coordinates\\nwhile(lat_lng_coords is None):\\n  g = geocoder.google('{}, Toronto, Ontario'.format(postal_code))\\n  lat_lng_coords = g.latlng\\n\\nlatitude = lat_lng_coords[0]\\nlongitude = lat_lng_coords[1]\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"import geocoder # import geocoder\n",
    "\n",
    "# initialize your variable to None\n",
    "lat_lng_coords = None\n",
    "\n",
    "# loop until you get the coordinates\n",
    "while(lat_lng_coords is None):\n",
    "  g = geocoder.google('{}, Toronto, Ontario'.format(postal_code))\n",
    "  lat_lng_coords = g.latlng\n",
    "\n",
    "latitude = lat_lng_coords[0]\n",
    "longitude = lat_lng_coords[1]\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Couldn't figure out the error, So I used the csv given in the link  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</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>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code   Latitude  Longitude\n",
       "0         M1B  43.806686 -79.194353\n",
       "1         M1C  43.784535 -79.160497\n",
       "2         M1E  43.763573 -79.188711\n",
       "3         M1G  43.770992 -79.216917\n",
       "4         M1H  43.773136 -79.239476"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd # library for data analsysis\n",
    "import numpy as np # library to handle data in a vectorized manner\n",
    "\n",
    "link = \"http://cocl.us/Geospatial_data\"\n",
    "df1 = pd.read_csv(link)\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the data frames have 103 rows and 3 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changing the column name Postal code to Postcode to merge the two data frames together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Postcode', 'Latitude', 'Longitude']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns = ['Postcode','Latitude','Longitude']\n",
    "\n",
    "cols = df1.columns.tolist()\n",
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in the CSV file saved in the previous assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood ,Morningside ,West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode      Borough                           Neighbourhood\n",
       "0      M1B  Scarborough                          Rouge ,Malvern\n",
       "1      M1C  Scarborough  Highland Creek ,Rouge Hill ,Port Union\n",
       "2      M1E  Scarborough       Guildwood ,Morningside ,West Hill\n",
       "3      M1G  Scarborough                                  Woburn\n",
       "4      M1H  Scarborough                               Cedarbrae"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "link = \"https://raw.githubusercontent.com/Shekhar-rv/Coursera_Capstone/master/df_can.csv\"\n",
    "df = pd.read_csv(link,index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merging the two data frames together based on their Postcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</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>43.806686</td>\n",
       "      <td>-79.194353</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>43.784535</td>\n",
       "      <td>-79.160497</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>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode      Borough                           Neighbourhood   Latitude  \\\n",
       "0      M1B  Scarborough                          Rouge ,Malvern  43.806686   \n",
       "1      M1C  Scarborough  Highland Creek ,Rouge Hill ,Port Union  43.784535   \n",
       "2      M1E  Scarborough       Guildwood ,Morningside ,West Hill  43.763573   \n",
       "3      M1G  Scarborough                                  Woburn  43.770992   \n",
       "4      M1H  Scarborough                               Cedarbrae  43.773136   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.194353  \n",
       "1 -79.160497  \n",
       "2 -79.188711  \n",
       "3 -79.216917  \n",
       "4 -79.239476  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = pd.merge(df, df1, on='Postcode')\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the file as a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_new.to_csv(r'df_final.csv')"
   ]
  }
 ],
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