{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston=pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/MASS/Boston.csv\")"
   ]
  },
  {
   "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>Unnamed: 0</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>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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</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>2</td>\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</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>3</td>\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</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     crim    zn  indus  chas    nox     rm   age     dis  rad  \\\n",
       "0           1  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1   \n",
       "1           2  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2   \n",
       "2           3  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2   \n",
       "3           4  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3   \n",
       "4           5  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3   \n",
       "\n",
       "   tax  ptratio   black  lstat  medv  \n",
       "0  296     15.3  396.90   4.98  24.0  \n",
       "1  242     17.8  396.90   9.14  21.6  \n",
       "2  242     17.8  392.83   4.03  34.7  \n",
       "3  222     18.7  394.63   2.94  33.4  \n",
       "4  222     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston=boston.drop(\"Unnamed: 0\",1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "var=boston.groupby('chas').medv.mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8078ba8>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(var.chas,var.medv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ebe0f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.xlabel(\"Charles River Facing Tract\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ffc5c0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.ylabel(\"Median Prices\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dSktLKS0t3WJdeXl5Rt7Lku2RMLPxwGB3L01YfznQyd1/mXQRIWycD7SLH+dj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\nt1wS274ZaAY8RxiPYxSwB3C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Vu5p9K4/7qyj+P9KipbpF13CI/CDZvu+ZlQ/c/TszW0n4\nqz4czKwrcDVwENCA0Kf/buIxfOt99kcS/qJfbrZFefUJX2QQ/sr9m5ldBbwGPOvun26j9vuBvwP7\nxR4P2sZrngYeMLPW7v4OobVjhofWD4CWwK/MbFXC6zxWZ2XXy4xt1FVpDqGlofJDf3/Ng5n9FPgf\n4CTC+a5LOB8HxdXymbsvrOF7AXzqW7ZqLeaHf8tDYu8xrXKju38b3520nX50TszsRuAK4EDCZ9sR\neDO2uTGwB/B6mt5fJCsUOER+MJ/wBdkMeLEG+yd2Wzixbkozu4zwRV4CvE34K/0moHXCa9Zs4z12\nJbSCtOPHgehbAHe/08yeBM4m/HX/BzO7zN239hmWxQLGp2Z2CTDTzKa7e5XXkbj7EjN7ndCN8g6h\nq+L/EuocFfuMiXUujnu8rc9baYO7L6hm2xOEv+K7AZ8Twsh0frhIc20N3yNetf+WWbDFOTGz3wF/\nILQqlRF+dv4A/Cy2SyqfTyRyuoZDJMbdVwBjga6V1wPEi12nUFNtgCnu/pi7vx/7cj9kWy+qQhmh\nH36zu3+asHx/14aHi1sfdvf2wEhCy0qNuPsXhGsH7tnGrk8Cl5rZcYQ7L/6ZUOfhhJaFxDrT/QXZ\nBhjg7mPdfTYhHDSK2/4B0NjMDk7T+31C7ELiyhWx633+K4ljJF4zsjVtgPHuPjTuZ+fQ7w/k/h/C\nhainVPP6yvBUN4n3FMk4BQ6RLXUl/KJ+x8x+bWb/ZWbNYneYvJXEceYDvzCz02N3edxF3BdWTbn7\na8BU4AUzO83MGptZGzP7HzNrFbujYaCZtTOzg8zshNj7zEryrR4Gzt3G3S0jgN0Jd/JMiH3xVfo/\nwoWnT5vZL8zs52bW3syGWEJfUBrMB64ys6ZmdjzhgsvvQ427v044ZyPM7BQz+5mZnWlmp6byZu5e\nTmhVeSh2no8A/kb4Yq9pkEjmHMwHTjCzk82siZndT7h4NN4fgNvM7PdmdoiZFZnZdbF6NxBalU6z\nMKZMMkFZJGMUOETixJrxWxEucHyAcJ3GOMJdCTfG71rVy+MeP0b4gn6a0KXyE7bsgthqGQnPzwIm\nEe7MmAs8RbheYQnhL+89CV+6c2PvN5rwhVTT4xNrKRgL3FXti9xXA/8iXFfyRMK2xcAJhN8pYwmt\nDA8BK9y98v2S+St/azoCexOuhxkSe59vEva5MLb9aeAj4G627/ddd0JX0mjC7byvE65L+dHdTNWo\n7rNXtX4A4WduBDCFUPeQLV7kPohwoe6NhM/3AuF6j/h6LyTcvfQmIjnAfvhdICIiNWFmuwJfAte7\n+z+irkckH+iiURGRbYh1NR1KuFPlJ4QxWDYSLpQVkRpQ4BAR2TYj3IFzKOGumBnASbHrO0SkBtSl\nIiIiIhmni0ZFREQk4xQ4RP5/u3UsAAAAADDI33oU+4oiAHbCAQDshAMA2AkHALATDgBgJxwAwE44\nAIBdGHBza7lFhz8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eb3a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}