Assignment-2(Part-1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Point- 1. Creating a new Notebook for this assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wikipedia\n",
      "  Downloading https://files.pythonhosted.org/packages/67/35/25e68fbc99e672127cc6fbb14b8ec1ba3dfef035bf1e4c90f78f24a80b7d/wikipedia-1.4.0.tar.gz\n",
      "Collecting beautifulsoup4 (from wikipedia)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/66/25/ff030e2437265616a1e9b25ccc864e0371a0bc3adb7c5a404fd661c6f4f6/beautifulsoup4-4.9.1-py3-none-any.whl (115kB)\n",
      "\u001b[K     |████████████████████████████████| 122kB 6.4MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: requests<3.0.0,>=2.0.0 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from wikipedia) (2.23.0)\n",
      "Collecting soupsieve>1.2 (from beautifulsoup4->wikipedia)\n",
      "  Downloading https://files.pythonhosted.org/packages/6f/8f/457f4a5390eeae1cc3aeab89deb7724c965be841ffca6cfca9197482e470/soupsieve-2.0.1-py3-none-any.whl\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2020.6.20)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (1.25.9)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2.9)\n",
      "Building wheels for collected packages: wikipedia\n",
      "  Building wheel for wikipedia (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jupyterlab/.cache/pip/wheels/87/2a/18/4e471fd96d12114d16fe4a446d00c3b38fb9efcb744bd31f4a\n",
      "Successfully built wikipedia\n",
      "Installing collected packages: soupsieve, beautifulsoup4, wikipedia\n",
      "Successfully installed beautifulsoup4-4.9.1 soupsieve-2.0.1 wikipedia-1.4.0\n",
      "Collecting lxml\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/55/6f/c87dffdd88a54dd26a3a9fef1d14b6384a9933c455c54ce3ca7d64a84c88/lxml-4.5.1-cp36-cp36m-manylinux1_x86_64.whl (5.5MB)\n",
      "\u001b[K     |████████████████████████████████| 5.5MB 9.0MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: lxml\n",
      "Successfully installed lxml-4.5.1\n"
     ]
    }
   ],
   "source": [
    "!pip install wikipedia\n",
    "!pip install lxml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import wikipedia as wp\n",
    "import lxml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Point- 2. Import Wikipedia table and create a List of Dictionary\n",
    "#### Use the Notebook to build the code to scrape the following Wikipedia page, https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "URL = (\"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\")\n",
    "res = requests.get(URL).text\n",
    "soup = BeautifulSoup(res,'lxml')\n",
    "datalist = []\n",
    "for items in soup.find('table', class_='wikitable').find_all('tr')[1::1]:\n",
    "    data = items.find_all(['th','td'])\n",
    "    try:\n",
    "        PostalCode =  str.rstrip(data[0].text)\n",
    "        Borough = str.rstrip(data[1].text)\n",
    "        Neighborhood = str.rstrip(data[2].text)\n",
    "    except IndexError:pass\n",
    "    datalist.append({'PostalCode':PostalCode,'Borough':Borough,'Neighborhood':Neighborhood})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Point- 3. To create the above dataframe:\n",
    "#### The dataframe will consist of three columns: PostalCode, Borough, and Neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>M5Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>M6Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>M7Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>M9Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PostalCode           Borough  \\\n",
       "0          M1A      Not assigned   \n",
       "1          M2A      Not assigned   \n",
       "2          M3A        North York   \n",
       "3          M4A        North York   \n",
       "4          M5A  Downtown Toronto   \n",
       "..         ...               ...   \n",
       "175        M5Z      Not assigned   \n",
       "176        M6Z      Not assigned   \n",
       "177        M7Z      Not assigned   \n",
       "178        M8Z         Etobicoke   \n",
       "179        M9Z      Not assigned   \n",
       "\n",
       "                                          Neighborhood  \n",
       "0                                         Not assigned  \n",
       "1                                         Not assigned  \n",
       "2                                            Parkwoods  \n",
       "3                                     Victoria Village  \n",
       "4                            Regent Park, Harbourfront  \n",
       "..                                                 ...  \n",
       "175                                       Not assigned  \n",
       "176                                       Not assigned  \n",
       "177                                       Not assigned  \n",
       "178  Mimico NW, The Queensway West, South of Bloor,...  \n",
       "179                                       Not assigned  \n",
       "\n",
       "[180 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(datalist)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Only process the cells that have an assigned borough. Ignore cells with a borough that is Not assigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>M8X</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>The Kingsway, Montgomery Road, Old Mill North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>M4Y</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Church and Wellesley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>M7Y</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Business reply mail Processing Centre, South C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>M8Y</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Old Mill South, King's Mill Park, Sunnylea, Hu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PostalCode           Borough  \\\n",
       "2          M3A        North York   \n",
       "3          M4A        North York   \n",
       "4          M5A  Downtown Toronto   \n",
       "5          M6A        North York   \n",
       "6          M7A  Downtown Toronto   \n",
       "..         ...               ...   \n",
       "160        M8X         Etobicoke   \n",
       "165        M4Y  Downtown Toronto   \n",
       "168        M7Y      East Toronto   \n",
       "169        M8Y         Etobicoke   \n",
       "178        M8Z         Etobicoke   \n",
       "\n",
       "                                          Neighborhood  \n",
       "2                                            Parkwoods  \n",
       "3                                     Victoria Village  \n",
       "4                            Regent Park, Harbourfront  \n",
       "5                     Lawrence Manor, Lawrence Heights  \n",
       "6          Queen's Park, Ontario Provincial Government  \n",
       "..                                                 ...  \n",
       "160      The Kingsway, Montgomery Road, Old Mill North  \n",
       "165                               Church and Wellesley  \n",
       "168  Business reply mail Processing Centre, South C...  \n",
       "169  Old Mill South, King's Mill Park, Sunnylea, Hu...  \n",
       "178  Mimico NW, The Queensway West, South of Bloor,...  \n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_assigned = df[df['Borough'] != \"Not assigned\"]\n",
    "df_assigned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More than one neighborhood can exist in one postal code area. For example, in the table on the Wikipedia page, you will notice that M5A is listed twice and has two neighborhoods: Harbourfront and Regent Park. These two rows will be combined into one row with the neighborhoods separated with a comma as shown in row 11 in the above table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Malvern, Rouge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge Hill, Port Union, Highland Creek</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>M9N</td>\n",
       "      <td>York</td>\n",
       "      <td>Weston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M9P</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Westmount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>M9R</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Kingsview Village, St. Phillips, Martin Grove ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>South Steeles, Silverstone, Humbergate, Jamest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>M9W</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Northwest, West Humber - Clairville</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PostalCode      Borough                                       Neighborhood\n",
       "0          M1B  Scarborough                                     Malvern, Rouge\n",
       "1          M1C  Scarborough             Rouge Hill, Port Union, Highland Creek\n",
       "2          M1E  Scarborough                  Guildwood, Morningside, West Hill\n",
       "3          M1G  Scarborough                                             Woburn\n",
       "4          M1H  Scarborough                                          Cedarbrae\n",
       "..         ...          ...                                                ...\n",
       "98         M9N         York                                             Weston\n",
       "99         M9P    Etobicoke                                          Westmount\n",
       "100        M9R    Etobicoke  Kingsview Village, St. Phillips, Martin Grove ...\n",
       "101        M9V    Etobicoke  South Steeles, Silverstone, Humbergate, Jamest...\n",
       "102        M9W    Etobicoke                Northwest, West Humber - Clairville\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df = df_assigned.pivot_table(index=['PostalCode','Borough'],aggfunc=','.join).reset_index()\n",
    "joined_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If a cell has a borough but a Not assigned neighborhood, then the neighborhood will be the same as the borough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  \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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M8A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M2B</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M7B</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>M4Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>M5Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>M6Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>M7Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>M9Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PostalCode       Borough  Neighborhood\n",
       "0          M1A  Not assigned  Not assigned\n",
       "1          M2A  Not assigned  Not assigned\n",
       "7          M8A  Not assigned  Not assigned\n",
       "10         M2B  Not assigned  Not assigned\n",
       "15         M7B  Not assigned  Not assigned\n",
       "..         ...           ...           ...\n",
       "174        M4Z  Not assigned  Not assigned\n",
       "175        M5Z  Not assigned  Not assigned\n",
       "176        M6Z  Not assigned  Not assigned\n",
       "177        M7Z  Not assigned  Not assigned\n",
       "179        M9Z  Not assigned  Not assigned\n",
       "\n",
       "[77 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_notassigned = df[df['Neighborhood'] == \"Not assigned\"]\n",
    "df_notassigned['Neighborhood'] = df_notassigned['Borough']\n",
    "df_notassigned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean your Notebook and add Markdown cells to explain your work and any assumptions you are making."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exporting the dataframe in csv file so that in 2nd question it can be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In the last cell of your notebook, use the .shape method to print the number of rows of your dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_df.to_csv('dataframe1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}