Visuals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Housing Data\n",
    "\n",
    "Data Source - https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
    "\n",
    "1. Any missing data.\n",
    "2. Graph home median value vs average number of rooms. \n",
    "3. Graph home median values vs percentage of lower status.\n",
    "4. Median value of a home. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(505, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.02731   0.00   7.070  0  0.4690  6.4210  78...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02729   0.00   7.070  0  0.4690  7.1850  61...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03237   0.00   2.180  0  0.4580  6.9980  45...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00\n",
       "0   0.02731   0.00   7.070  0  0.4690  6.4210  78...                                              \n",
       "1   0.02729   0.00   7.070  0  0.4690  7.1850  61...                                              \n",
       "2   0.03237   0.00   2.180  0  0.4580  6.9980  45...                                              "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were three errors:\n",
    "* All the data appeared in one column. That is, the columns were not \"delimited\" correctly\n",
    "* The first line got mistook as a header\n",
    "* The columns are missing their header names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correcting Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names = ['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT','MEDV']\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/'+\n",
    "                 'housing.data',header=None,delim_whitespace=True,names=names,na_values='?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  MEDV  \n",
       "0     15.3  396.90   4.98  24.0  \n",
       "1     17.8  396.90   9.14  21.6  \n",
       "2     17.8  392.83   4.03  34.7  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Data\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph home median value vs average number of rooms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.array(df['MEDV'].dropna())\n",
    "y = np.array(df['RM'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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I73e7R/3GxVRUTp6K76RR8SqMD78HwFIAnwdwc/XfX4W5uIjMAPDbAO6O28E0iepLXHPx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\nOYEGnxBCcgINPiGE5IT/DzCkz0xztzsUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f0c310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'o')\n",
    "plt.xlabel('Median Value')\n",
    "plt.ylabel('Avg # of Room')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The avg number of room increses as the median value increases. \n",
    "They are directly propotional, thus Median Value and Avg # of Room are directly correlated.\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph home median values vs percentage of lower status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.array(df['MEDV'].dropna())\n",
    "y = np.array(df['LSTAT'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ThtFMuFiPTwq+EZFpwKJsxGk8/FXsrv0amx8Qh1/wLY7DZ+Zob0vsIBpJC5Br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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1173b71d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'o')\n",
    "plt.xlabel('Median Value')\n",
    "plt.ylabel('% lower status of the population')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LSTAT value decreases as the median value increases. They are inversly propotional, thus the Median Value and the LSTAT are inversly correlated.\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean and Median value of a home "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean =  21.2, Median =  21.2 \n"
     ]
    }
   ],
   "source": [
    "mx = np.mean(x)\n",
    "median = np.median(x)\n",
    "print ('Mean = {1:5.1f}, Median = {1:5.1f} '.format(mx,median))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}