Created on Cognitive Class Labs

Week3_assignment_Toronto clustering.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Segmenting and Clustering Neighborhoods in Toronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "!pip install lxml\n",
    "\n",
    "print('All imported!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 1: Web scraping Wikipedia HTML tables\n",
    "\n",
    "web site: https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing the tables of the target webpage\n",
    "data = pd.read_html('http://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M', header=0)\n",
    "df = data[0]\n",
    "\n",
    "# Changing column names\n",
    "df.columns = ['PostalCode', 'Borough', 'Neighborhood']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 2: Performing required operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data cleaning per the instructions\n",
    "df.drop(df[df.Borough == 'Not assigned'].index, inplace=True)\n",
    "df.loc[df.Neighborhood == 'Not assigned', 'Neighborhood'] = df['Borough']\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df = df.groupby('PostalCode').agg({'Borough':'first','Neighborhood': ', '.join}).reset_index()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3: Verifying results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Testing if grouping works, by comparing with table given in the instructions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['PostalCode'] == 'M9V']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Printing number of rows of the resulting table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = df.shape\n",
    "print('Final table has',df.shape[0], 'rows.')"
   ]
  }
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