2-introduction-to-pandas-matplotlib-and-regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# previous seminar was about numpy\n",
    "import numpy\n",
    "\n",
    "# we already learnt something from sklearn\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# today we learn about pandas\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting in Jupyter with matplotlib\n",
    "\n",
    "below we use a **magic function**. It is a term!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# magic says: activate matplotlib, show plots inline\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.  -1.5  0.   1.   3.   4. ]\n",
      "[  9.     2.25   0.     1.     9.    16.  ]\n"
     ]
    }
   ],
   "source": [
    "x = numpy.array([-3, -1.5, 0, 1, 3, 4])\n",
    "\n",
    "print x\n",
    "print x ** 2 # squared each element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc2d255fd90>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dCUtXNbsYFmLJErj2Wli7Fg46KJJLikgraiUsj0iWU8zsQDO738wazOwFMzu+lPcrlQYt\ni8RPrYTRKnVN/E5gmbsPBI4DGkoPqTS33gr/+78aIiESh+ZWQu1KGJ2il1PM7ADgGXfv38lxkS2n\nNNOgZZHoNe9K+OSTcPTRcUeTflEsp/QFtprZAjNba2b/bmZ7l/B+ZaNByyLRarkroRJ4tEq5Y7Mr\nMAS4xt3XmNlsYDIwtfWBdXV1ux5nMhkymUwJl81Py0HLjz8OXVN7b6pIsqmVsDyy2SzZbLbg80pZ\nTukFrHL3frnnpwDXu/vZrY6LfDmlmQYti1SWWgkrp+LLKe7eCLxuZs1bup8OvFjs+1VCly6hxWnR\nIvjFL+KORqS6NDWF7WT794dp0+KOpnaV1CduZscB9wDdgFeBK939nVbHxFaJN1u5Es47D556Cvr0\niTUUkaoxZUoYrfbYY+pEqYSavNmnI7NmwU9+Ev7Q7bln3NGIpFt9fViiXL1am1pVipJ4K+5ht8ND\nDoG5c+OORiS91EoYjZraACsfzYOWf/lLDVoWKZZaCZOnZpI4wIEHatCySLHUSphMNZXEQYOWRYqh\nAcfJVTNr4i1p0LJI/pqawsZy3btrV8IoaU28Axq0LJK/G28M21doV8Jkqtmb0TVoWaRzGnCcfDVZ\niTfToGWR9mnAcTrU5Jp4axq0LPJRGnAcP62JF0CDlkV2UythuqgSz9GgZRHtSpgkuu2+CBq0LLWs\nuZWwW7ew86eWFuOlJF6kf/5neP75sHVtFy02SQ3RroTJojXxImnQstQiDThOL1XibdCgZakl2pUw\nmVSJl0CDlqVWaFfC9FMSb0fLQcs7dsQdjUj5qZWwOmg5pQNNTTBmDAwerDVyqS5qJUw+daeUydat\nMGQI3H03nHVW3NGIlE6thOkQ2Zq4mXUxs7VmtqTU90qinj3DJKCrrgp3dYqkXfOuhPPnK4FXg3Ks\niU8AXizD+yTWiSfC5Mnwd38HH34YdzQixVMrYfUpKYmbWW9gDHBPecJJrokT4Ygj4JvfjDsSkeJo\nV8LqVGolPguYBKR30TtPGrQsaaZWwupV9FAIMzsTaHT3Z80sA7S7ulZXV7frcSaTIZPJFHvZWDUP\nWh4xIszq1F8GSQO1EqZDNpslm80WfF7R3SlmdhtwKbAD2BvYH/iZu1/e6rhUd6e0Zd68sG3tU0/B\nPvvEHY1I+9RKmF6Rthia2TDgm+4+to3vVV0S16BlSQO1EqabbruvIA1aljRQK2Ft0M0+JWhoCIOW\nH3tMg5YlWerrw13Gq1erEyWtVIlHQIOWJYnUSlhbVImXgQYtS1JowHH1UCUeIQ1aliRQK2FtUiVe\nJhq0LHFSK2H10S6GMdCgZYmDWgmrk5J4TDRoWaKmAcfVSWviMdGgZYnSPfdoV8Jap0q8AjRoWaKg\nAcfVTZV4jA47DH70I7j0Ug1alsrQroTSTEm8Qk4/XYOWpTLUSigtaTmlgpoHLQ8aBDNnxh2NVAO1\nEtYOdackhAYtS7molbC2aE08ITRoWcpFuxJKW5TEI6BBy1IqtRJKe7ScEhH3sNvhIYfA3LlxRyNp\nolbC2qTllITRoGUphloJpTNK4hFqHrQ8fjxs2BB3NJJ0aiWUfCiJR2zQILjttrC0sm1b3NFIUr3/\nPpxzDlx2GVx+eefHS+0qZdp9b2Ah0AtoAua5+8d21Naa+Mdp0LJ0RK2EAhH0iZvZwcDB7v6sme0H\n/A44x91fanWckngb/u//wnzObt1g4kQ4//zwWES7EgpE8MGmu29x92dzj98DGoDDin2/WrPvvvD0\n06H18Hvfg379wl2df/5z3JFJnNRKKIUqy5q4mfUBBgFPleP9asUee8C550I2GwZKvPQS9O8PX/sa\nNDTEHZ1E7fHHww09GnAshSg5ieeWUh4AJuQqcinC4MHwgx+E5N2rV9gfY/To0JLY1BR3dFJpaiWU\nYpV0s4+ZdQV+ATzi7ne2c4xPnTp11/NMJkMmkyn6mrXigw/g3nth9uxwl+eECaFTYd99445Myq2x\nMcxlnTZNnSi1LJvNks1mdz2fNm1a5TfAMrOFwFZ3/0YHx+iDzRK4hzv1Zs+GFSvCHizXXAOHHx53\nZFIO2pVQ2hNFd8rJwJPAesBzXze4+y9bHackXiavvBJu2f/hD8Nf+okTQwUn6aRWQumItqKtYu++\nG27hnzMHPvUptSimlVoJpSNK4jVg505YujQstbzySlhm+Yd/gE9+Mu7IpDP33BNaSlevVieKtE0b\nYNUAtSimk1oJpZyUxKuEWhTTQa2EUm5aTqlSalFMHrUSSiG0Ji6AWhSTQq2EUiitiQsQ2taGDYP/\n+I/wIdqHH8Jxx8FFF4XnUnlNTaHy7tcvVOEi5aRKvAapRTFaaiWUYmg5RTqlFsXKUyuhFEvLKdKp\n9loUr75aLYrloFZCiYKSuAAfbVE8+GC1KBbLPfwefvvbaiWUaGg5RdqkFsX8vf9++Gnm4YfD186d\nYcDxpZfCySfHHZ2kldbEpSzUoti2P/wBli0LSfvJJ8MA7DPPDF/HHKPNrKR0SuJSdrW8i+L27bBy\n5e7E3dgIZ5wRkvbIkdCjR9wRSrVREpeKqZUWxTffhEceCYl7+XLo23d3tT10aPhgWKRSlMSl4tpq\nUfzqV+Ggg+KOrDhNTbB2bai0ly2DDRvg9NND0j7jDDjkkLgjlFqiJC6ReuYZuPNOeOihMOhgwgQY\nODDuqDr3zjuhyn744VB19+gBY8aExH3KKdC9e9wRSq1SEpdYbNkC3/te+Bo0KCy1jBwJXRLSzOoe\n+uGbO0nWrAnJujlx9+sXd4QigZK4xCpJLYotWwCXLYMdO0LCHjMGTjtNbZOSTErikghxtSi21wI4\nZgwce6xaACX5IkniZjYamE2487Pe3f+1jWOUxAWobIvi9u2watXuZRK1AEraVXzvFDPrAswFRgHH\nABeb2V8X+35Jlc1m4w6hJEmKv39/mDULNm0KyXvcuPDrvfeGJNxaZ7G/+SYsXBg+SO3VC667Dvbc\nM2w6tWXL7u/FlcCT9HtfDMWfDqV83PQ3wEZ3/4O7bwfuBc4pT1jJkfY/CEmM/4ADQhW+cSNMnhw+\nBO3XL+z299Zbu49rHXtTU/gg8pZb4PjjYcCA0A0zahS88AL87nfheyeckIwe7iT+3hdC8adD1xLO\nPQx4vcXz/yIkdpG8NO+ieO65u1sUjzpqd4si7G4BXLYstAAeeGBYIpkxQy2AIlBaEhcpm+ZdFJtb\nFIcPD5X3HXeETaTOPDNs69q/f9yRiiRL0R9smtkJQJ27j849nwx46w83zUyfaoqIFKGi3Slmtgew\nATgd+BPwNHCxu2ucgIhIRIpeTnH3nWY2HniU3S2GSuAiIhGq+M0+IiJSOZHsaGFmt5jZOjN7xsx+\naWYHR3HdcjGz282swcyeNbMHzeyAuGPKl5ldYGbPm9lOMxsSdzz5MrPRZvaSmb1sZtfHHU8hzKze\nzBrN7Lm4YymGmfU2s1+b2Qtmtt7M/inumPJlZnua2VO5XLPezKbGHVMxzKyLma01syWdHRvVtkS3\nu/tx7j4YeBhI22/so8Ax7j4I2AhMiTmeQqwHzgOeiDuQfFXBjWQLCLGn1Q7gG+5+DHAicE1afv/d\n/UNgeC7XDALOMLM0tj5PAF7M58BIkri7v9fi6b5Aqkbvuvtj7t4c82qgd5zxFMLdN7j7RiBNu4Wk\n+kYyd18BvB13HMVy9y3u/mzu8XtAA+G+kFRw9225h3sSPvdL1ZqxmfUGxgD35HN8ZBuEmtm3zGwz\nMA74l6iuWwF/DzwSdxBVrq0byVKTRKqJmfUhVLRPxRtJ/nJLEc8AW4Dl7v7buGMq0CxgEnn+41O2\nJG5my83suRZf63O/ng3g7je5+xHAIuDacl23XDqLP3fMjcB2d18cY6gfk0/sIoUys/2AB4AJrX6a\nTjR3b8otp/QGjjezz8QdU77M7EygMfeTkJHHT9Blu2PT3UfkeehiYBlQV65rl0Nn8ZvZlwk/4pwW\nSUAFKOD3Pi3+CBzR4nnv3GsSETPrSkjgP3L3h+KOpxju/q6Z/QYYTZ7rywlwMjDWzMYAewP7m9lC\nd7+8vROi6k45qsXTcwlrbKmR23J3EjA298FJWqVlXfy3wFFmdqSZdQcuAjr9lD5h8qqiEmw+8KK7\n3xl3IIUws55mdmDu8d7ACOCleKPKn7vf4O5HuHs/wp/7X3eUwCG6NfGZuR/vnwW+SPjkNU3uAvYD\nlufafu6OO6B8mdm5ZvY6cALwCzNL/Hq+u+8Emm8kewG4N003kpnZYmAlMMDMNpvZlXHHVAgzOxm4\nBDgt16q3NlfIpMEhwG9yueYp4FfuvizmmCpKN/uIiKRYQsbXiohIMZTERURSTElcRCTFlMRFRFJM\nSVxEJMWUxEVEUkxJXEQkxZTERURS7P8Bgs1c9uvz63AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2fc1e9990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S1GGGgCR1mCEgSR1mCEhShxkCktRhhoAkddhxhUCSfUn+JMnzSXY0bWck2Z7k5SRPJDl9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\nzBCQpA4zBCSpwwwBSeqw/w/RnBSrlqddgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2fc1e9950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution = numpy.random.normal(loc=1., scale=2, size=10000)\n",
    "plt.hist(distribution)\n",
    "pass # it is the way to suppress printing output of last line. pass = 'do nothing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vA5eSflpwbL7G5UtfCy6zOPT83WeN5y/0OH4ZuJT004Jj84tpXH53wzLrEvXd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\nn0x+80wkk5EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2fc1e9910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(numpy.random.normal(loc=5., scale=2, size=10000), bins=30, alpha=0.4, normed=True, label='first')\n",
    "plt.hist(numpy.random.normal(loc=7., scale=3, size=10000), bins=30, alpha=0.4, normed=True, label='second')\n",
    "plt.hist(numpy.random.normal(loc=0., scale=1, size=10000), bins=30, alpha=0.4, normed=True, label='third')\n",
    "plt.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc2a3b2ad90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BIC20SAMwsKhdILKYMJfVpLykJyey8El3TLIEkCeCYABdRQ1QNgeOgaQzWln5SqJdLbLq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\nPDGAzLBYBpCQqDW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6D3ot8OGKfs8rq2MPQDLGyFpret6oX5Sc1I/IBAOllWbGKqnHjjOZ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d243a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Large scatter plot\n",
    "plt.figure(figsize=[12, 10])\n",
    "plt.scatter(numpy.random.normal(size=300), numpy.random.normal(size=300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Magics\n",
    "\n",
    "magics are not needed so far, those are just for the information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.33 ms, sys: 20.6 ms, total: 29 ms\n",
      "Wall time: 13.7 ms\n",
      "CPU times: user 4.13 ms, sys: 0 ns, total: 4.13 ms\n",
      "Wall time: 4.2 ms\n"
     ]
    }
   ],
   "source": [
    "# sample two arrays of random numbers sampled from uniform [0, 1]\n",
    "a = numpy.random.random(size=1000000)\n",
    "%time b = numpy.random.random(size=1000000)\n",
    "# measure time needed for operation\n",
    "%time result = a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 24.2 ms, sys: 7.35 ms, total: 31.5 ms\n",
      "Wall time: 31.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# measure the time needed to run whole cell (%%, not %)\n",
    "a = numpy.random.random(size=1000000)\n",
    "b = numpy.random.random(size=1000000)\n",
    "result = a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    }
   ],
   "source": [
    "%%prun \n",
    "# profiling of complex code\n",
    "numpy.histogram(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# general info about magics in Jupyter\n",
    "%magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello Pandas\n",
    "\n",
    "Looking at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"fixed acidity\";\"volatile acidity\";\"citric acid\";\"residual sugar\";\"chlorides\";\"free sulfur dioxide\";\"total sulfur dioxide\";\"density\";\"pH\";\"sulphates\";\"alcohol\";\"quality\"\r\n",
      "7;0.27;0.36;20.7;0.045;45;170;1.001;3;0.45;8.8;6\r\n",
      "6.3;0.3;0.34;1.6;0.049;14;132;0.994;3.3;0.49;9.5;6\r\n",
      "8.1;0.28;0.4;6.9;0.05;30;97;0.9951;3.26;0.44;10.1;6\r\n",
      "7.2;0.23;0.32;8.5;0.058;47;186;0.9956;3.19;0.4;9.9;6\r\n",
      "7.2;0.23;0.32;8.5;0.058;47;186;0.9956;3.19;0.4;9.9;6\r\n",
      "8.1;0.28;0.4;6.9;0.05;30;97;0.9951;3.26;0.44;10.1;6\r\n",
      "6.2;0.32;0.16;7;0.045;30;136;0.9949;3.18;0.47;9.6;6\r\n",
      "7;0.27;0.36;20.7;0.045;45;170;1.001;3;0.45;8.8;6\r\n",
      "6.3;0.3;0.34;1.6;0.049;14;132;0.994;3.3;0.49;9.5;6\r\n"
     ]
    }
   ],
   "source": [
    "!head data/2-winequality-white.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14</td>\n",
       "      <td>132</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47</td>\n",
       "      <td>186</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47</td>\n",
       "      <td>186</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.045</td>\n",
       "      <td>30</td>\n",
       "      <td>136</td>\n",
       "      <td>0.9949</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14</td>\n",
       "      <td>132</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.044</td>\n",
       "      <td>28</td>\n",
       "      <td>129</td>\n",
       "      <td>0.9938</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.45</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "5            8.1              0.28         0.40             6.9      0.050   \n",
       "6            6.2              0.32         0.16             7.0      0.045   \n",
       "7            7.0              0.27         0.36            20.7      0.045   \n",
       "8            6.3              0.30         0.34             1.6      0.049   \n",
       "9            8.1              0.22         0.43             1.5      0.044   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                   45                   170   1.0010  3.00       0.45   \n",
       "1                   14                   132   0.9940  3.30       0.49   \n",
       "2                   30                    97   0.9951  3.26       0.44   \n",
       "3                   47                   186   0.9956  3.19       0.40   \n",
       "4                   47                   186   0.9956  3.19       0.40   \n",
       "5                   30                    97   0.9951  3.26       0.44   \n",
       "6                   30                   136   0.9949  3.18       0.47   \n",
       "7                   45                   170   1.0010  3.00       0.45   \n",
       "8                   14                   132   0.9940  3.30       0.49   \n",
       "9                   28                   129   0.9938  3.22       0.45   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  \n",
       "5     10.1        6  \n",
       "6      9.6        6  \n",
       "7      8.8        6  \n",
       "8      9.5        6  \n",
       "9     11.0        6  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pandas.read_csv('data/2-winequality-white.csv', sep=';')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split into description and target\n",
    "y = data['quality']\n",
    "X = data.drop('quality', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>6.3</td>\n",
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       "      <td>0.34</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47</td>\n",
       "      <td>186</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
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       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.045</td>\n",
       "      <td>30</td>\n",
       "      <td>136</td>\n",
       "      <td>0.9949</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14</td>\n",
       "      <td>132</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.044</td>\n",
       "      <td>28</td>\n",
       "      <td>129</td>\n",
       "      <td>0.9938</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.45</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "5            8.1              0.28         0.40             6.9      0.050   \n",
       "6            6.2              0.32         0.16             7.0      0.045   \n",
       "7            7.0              0.27         0.36            20.7      0.045   \n",
       "8            6.3              0.30         0.34             1.6      0.049   \n",
       "9            8.1              0.22         0.43             1.5      0.044   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                   45                   170   1.0010  3.00       0.45   \n",
       "1                   14                   132   0.9940  3.30       0.49   \n",
       "2                   30                    97   0.9951  3.26       0.44   \n",
       "3                   47                   186   0.9956  3.19       0.40   \n",
       "4                   47                   186   0.9956  3.19       0.40   \n",
       "5                   30                    97   0.9951  3.26       0.44   \n",
       "6                   30                   136   0.9949  3.18       0.47   \n",
       "7                   45                   170   1.0010  3.00       0.45   \n",
       "8                   14                   132   0.9940  3.30       0.49   \n",
       "9                   28                   129   0.9938  3.22       0.45   \n",
       "\n",
       "   alcohol  \n",
       "0      8.8  \n",
       "1      9.5  \n",
       "2     10.1  \n",
       "3      9.9  \n",
       "4      9.9  \n",
       "5     10.1  \n",
       "6      9.6  \n",
       "7      8.8  \n",
       "8      9.5  \n",
       "9     11.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operations on data with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#leaving only 8 rows for testing\n",
    "test_frame = data[:8].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14</td>\n",
       "      <td>132</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47</td>\n",
       "      <td>186</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47</td>\n",
       "      <td>186</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.045</td>\n",
       "      <td>30</td>\n",
       "      <td>136</td>\n",
       "      <td>0.9949</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "5            8.1              0.28         0.40             6.9      0.050   \n",
       "6            6.2              0.32         0.16             7.0      0.045   \n",
       "7            7.0              0.27         0.36            20.7      0.045   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                   45                   170   1.0010  3.00       0.45   \n",
       "1                   14                   132   0.9940  3.30       0.49   \n",
       "2                   30                    97   0.9951  3.26       0.44   \n",
       "3                   47                   186   0.9956  3.19       0.40   \n",
       "4                   47                   186   0.9956  3.19       0.40   \n",
       "5                   30                    97   0.9951  3.26       0.44   \n",
       "6                   30                   136   0.9949  3.18       0.47   \n",
       "7                   45                   170   1.0010  3.00       0.45   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  \n",
       "5     10.1        6  \n",
       "6      9.6        6  \n",
       "7      8.8        6  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0010\n",
       "1    0.9940\n",
       "2    0.9951\n",
       "3    0.9956\n",
       "4    0.9956\n",
       "5    0.9951\n",
       "6    0.9949\n",
       "7    1.0010\n",
       "Name: density, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_frame['density']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.001 ,  0.994 ,  0.9951,  0.9956,  0.9956,  0.9951,  0.9949,\n",
       "        1.001 ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting to numpy.array with .values\n",
    "test_frame['density'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.0\n",
       "1    6.3\n",
       "2    8.1\n",
       "3    7.2\n",
       "4    7.2\n",
       "Name: fixed acidity, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_frame['fixed acidity'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   citric acid  residual sugar  chlorides  free sulfur dioxide  \\\n",
       "0         0.36            20.7      0.045                   45   \n",
       "4         0.32             8.5      0.058                   47   \n",
       "7         0.36            20.7      0.045                   45   \n",
       "\n",
       "   total sulfur dioxide  \n",
       "0                   170  \n",
       "4                   186  \n",
       "7                   170  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing with numbers - iloc\n",
    "test_frame.iloc[[0, 4, 7], 2:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>volatile acidity</th>\n",
       "      <th>fixed acidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.27</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.30</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.28</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.23</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.23</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.28</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.32</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.27</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volatile acidity  fixed acidity\n",
       "0              0.27            7.0\n",
       "1              0.30            6.3\n",
       "2              0.28            8.1\n",
       "3              0.23            7.2\n",
       "4              0.23            7.2\n",
       "5              0.28            8.1\n",
       "6              0.32            6.2\n",
       "7              0.27            7.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing with named labels - loc\n",
    "test_frame.loc[:, ['volatile acidity', 'fixed acidity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>volatile acidity</th>\n",
       "      <th>fixed acidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.27</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.30</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.28</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.23</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.23</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.28</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.32</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.27</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volatile acidity  fixed acidity\n",
       "0              0.27            7.0\n",
       "1              0.30            6.3\n",
       "2              0.28            8.1\n",
       "3              0.23            7.2\n",
       "4              0.23            7.2\n",
       "5              0.28            8.1\n",
       "6              0.32            6.2\n",
       "7              0.27            7.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shortcut for previous\n",
    "test_frame[['volatile acidity', 'fixed acidity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>quality</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>pH</th>\n",
       "      <th>density</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>fixed acidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>170</td>\n",
       "      <td>45</td>\n",
       "      <td>0.045</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.27</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>186</td>\n",
       "      <td>47</td>\n",
       "      <td>0.058</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.23</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.9949</td>\n",
       "      <td>136</td>\n",
       "      <td>30</td>\n",
       "      <td>0.045</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.32</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quality  alcohol  sulphates    pH  density  total sulfur dioxide  \\\n",
       "0        6      8.8       0.45  3.00   1.0010                   170   \n",
       "3        6      9.9       0.40  3.19   0.9956                   186   \n",
       "6        6      9.6       0.47  3.18   0.9949                   136   \n",
       "\n",
       "   free sulfur dioxide  chlorides  residual sugar  citric acid  \\\n",
       "0                   45      0.045            20.7         0.36   \n",
       "3                   47      0.058             8.5         0.32   \n",
       "6                   30      0.045             7.0         0.16   \n",
       "\n",
       "   volatile acidity  fixed acidity  \n",
       "0              0.27            7.0  \n",
       "3              0.23            7.2  \n",
       "6              0.32            6.2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [start:end:step], some of arguments can be omitted.\n",
    "test_frame.iloc[::3, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>dioxide sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14</td>\n",
       "      <td>132</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47</td>\n",
       "      <td>186</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47</td>\n",
       "      <td>186</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.045</td>\n",
       "      <td>30</td>\n",
       "      <td>136</td>\n",
       "      <td>0.9949</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.6</td>\n",
       "      <td>6</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "5            8.1              0.28         0.40             6.9      0.050   \n",
       "6            6.2              0.32         0.16             7.0      0.045   \n",
       "7            7.0              0.27         0.36            20.7      0.045   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                   45                   170   1.0010  3.00       0.45   \n",
       "1                   14                   132   0.9940  3.30       0.49   \n",
       "2                   30                    97   0.9951  3.26       0.44   \n",
       "3                   47                   186   0.9956  3.19       0.40   \n",
       "4                   47                   186   0.9956  3.19       0.40   \n",
       "5                   30                    97   0.9951  3.26       0.44   \n",
       "6                   30                   136   0.9949  3.18       0.47   \n",
       "7                   45                   170   1.0010  3.00       0.45   \n",
       "\n",
       "   alcohol  quality  dioxide sum  \n",
       "0      8.8        6          215  \n",
       "1      9.5        6          146  \n",
       "2     10.1        6          127  \n",
       "3      9.9        6          233  \n",
       "4      9.9        6          233  \n",
       "5     10.1        6          127  \n",
       "6      9.6        6          166  \n",
       "7      8.8        6          215  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding new feature with vector operation\n",
    "test_frame['dioxide sum'] = test_frame['total sulfur dioxide'] + test_frame['free sulfur dioxide']\n",
    "test_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2945</th>\n",
       "      <td>4.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.016</td>\n",
       "      <td>39</td>\n",
       "      <td>150</td>\n",
       "      <td>0.98713</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.59</td>\n",
       "      <td>14.00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3367</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.033</td>\n",
       "      <td>30</td>\n",
       "      <td>114</td>\n",
       "      <td>0.98794</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.43</td>\n",
       "      <td>13.20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3370</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.033</td>\n",
       "      <td>30</td>\n",
       "      <td>114</td>\n",
       "      <td>0.98794</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.43</td>\n",
       "      <td>13.20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3673</th>\n",
       "      <td>5.4</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.029</td>\n",
       "      <td>25</td>\n",
       "      <td>93</td>\n",
       "      <td>0.98742</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.40</td>\n",
       "      <td>13.60</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3676</th>\n",
       "      <td>5.3</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.029</td>\n",
       "      <td>25</td>\n",
       "      <td>93</td>\n",
       "      <td>0.98742</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.40</td>\n",
       "      <td>13.60</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3710</th>\n",
       "      <td>4.7</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0.98722</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>13.60</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3773</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.009</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>0.98740</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.37</td>\n",
       "      <td>13.50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3904</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.036</td>\n",
       "      <td>33</td>\n",
       "      <td>106</td>\n",
       "      <td>0.98746</td>\n",
       "      <td>3.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>4.7</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.036</td>\n",
       "      <td>33</td>\n",
       "      <td>106</td>\n",
       "      <td>0.98746</td>\n",
       "      <td>3.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3918</th>\n",
       "      <td>6.4</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.037</td>\n",
       "      <td>31</td>\n",
       "      <td>113</td>\n",
       "      <td>0.98779</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.40</td>\n",
       "      <td>14.20</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4149</th>\n",
       "      <td>5.8</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.038</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>0.98711</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.29</td>\n",
       "      <td>13.90</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4645</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.034</td>\n",
       "      <td>49</td>\n",
       "      <td>158</td>\n",
       "      <td>0.98774</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>13.10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4789</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.037</td>\n",
       "      <td>24</td>\n",
       "      <td>118</td>\n",
       "      <td>0.98758</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.39</td>\n",
       "      <td>13.55</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "2945            4.9             0.330         0.31             1.2      0.016   \n",
       "3367            6.0             0.290         0.25             1.4      0.033   \n",
       "3370            6.0             0.290         0.25             1.4      0.033   \n",
       "3673            5.4             0.300         0.30             1.2      0.029   \n",
       "3676            5.3             0.300         0.30             1.2      0.029   \n",
       "3710            4.7             0.670         0.09             1.0      0.020   \n",
       "3773            5.0             0.610         0.12             1.3      0.009   \n",
       "3904            5.0             0.455         0.18             1.9      0.036   \n",
       "3915            4.7             0.455         0.18             1.9      0.036   \n",
       "3918            6.4             0.350         0.28             1.6      0.037   \n",
       "4149            5.8             0.240         0.28             1.4      0.038   \n",
       "4645            5.0             0.240         0.34             1.1      0.034   \n",
       "4789            5.5             0.620         0.33             1.7      0.037   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "2945                   39                   150  0.98713  3.33       0.59   \n",
       "3367                   30                   114  0.98794  3.08       0.43   \n",
       "3370                   30                   114  0.98794  3.08       0.43   \n",
       "3673                   25                    93  0.98742  3.31       0.40   \n",
       "3676                   25                    93  0.98742  3.31       0.40   \n",
       "3710                    5                     9  0.98722  3.30       0.34   \n",
       "3773                   65                   100  0.98740  3.26       0.37   \n",
       "3904                   33                   106  0.98746  3.21       0.83   \n",
       "3915                   33                   106  0.98746  3.21       0.83   \n",
       "3918                   31                   113  0.98779  3.12       0.40   \n",
       "4149                   40                    76  0.98711  3.10       0.29   \n",
       "4645                   49                   158  0.98774  3.32       0.32   \n",
       "4789                   24                   118  0.98758  3.15       0.39   \n",
       "\n",
       "      alcohol  quality  \n",
       "2945    14.00        8  \n",
       "3367    13.20        6  \n",
       "3370    13.20        6  \n",
       "3673    13.60        7  \n",
       "3676    13.60        7  \n",
       "3710    13.60        5  \n",
       "3773    13.50        5  \n",
       "3904    14.00        7  \n",
       "3915    14.00        7  \n",
       "3918    14.20        7  \n",
       "4149    13.90        7  \n",
       "4645    13.10        7  \n",
       "4789    13.55        6  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering dataset\n",
    "filtered_data = data[data['density'] < 0.988]\n",
    "filtered_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "(8, 13)\n",
      "Index([u'fixed acidity', u'volatile acidity', u'citric acid',\n",
      "       u'residual sugar', u'chlorides', u'free sulfur dioxide',\n",
      "       u'total sulfur dioxide', u'density', u'pH', u'sulphates', u'alcohol',\n",
      "       u'quality', u'dioxide sum'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print len(test_frame)\n",
    "print test_frame.shape \n",
    "print test_frame.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc2a834add0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r7L7vcTvR6TRXJl2NhnlzRlF+cSsNtUWUFR0mNqy119PFrhYdHc3qh77Crt02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cAPpZQffHJNSpkSQnwF2Mab4WQnhBBfOHlZ3gd8A3AAPxUngx0TUsr3Gwe+4HE4HKQ7DpNsXIRK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\nXKk3/53/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d2270fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X['total sulfur dioxide'], X['volatile acidity'], c=y, alpha=0.4)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "### Exercise 1\n",
    "plot histograms of all features in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "Let's classify wines in:\n",
    "   * good means quality >= 7\n",
    "   * bad means quality <= 4\n",
    "   * mediocre means quality five or six\n",
    "   \n",
    "Plot histograms of all features for good and bad wines. Which feature is the most discriminative?\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Exercise 3   \n",
    "Plot some scatter plots:\n",
    "   - alcohol vs fixed acidity for good and bad wines, what can we say about good wines?\n",
    "   - alcohol vs fixed acidity for quality=5 and quality=6\n",
    "   - alcohol vs density for good and bad wines. What can we conclude?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear models (complete exercises first!)\n",
    "\n",
    "computing mean squares error, desire smaller values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 \t0.564587791519\n",
      "0.001 \t0.568293991144\n",
      "0.01 \t0.573570550784\n",
      "0.1 \t0.574990791682\n",
      "1 \t0.575287625141\n",
      "10 \t0.576469007479\n",
      "100 \t0.593064358623\n"
     ]
    }
   ],
   "source": [
    "for alpha in [0., 0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    linear_reg = Ridge(alpha=alpha)\n",
    "    linear_reg.fit(trainX, trainY)\n",
    "    print alpha, '\\t', mean_squared_error(testY, linear_reg.predict(testX))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear models with nonlinear features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "# first, standatize all features by scaling (mean = 0, std = 1)\n",
    "X_extend = StandardScaler().fit_transform(X)\n",
    "X_extend = pandas.DataFrame(X_extend, columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for f in X_extend.columns:\n",
    "    X_extend[f + '_abs']  = numpy.abs(X_extend[f])\n",
    "    X_extend[f + '_tanh'] = numpy.tanh(X_extend[f])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4898, 33)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_extend.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainX_ex, testX_ex = train_test_split(X_extend, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 \t0.516931032212\n",
      "0.001 \t0.516931019462\n",
      "0.01 \t0.516930904672\n",
      "0.1 \t0.516929753694\n",
      "1 \t0.516918023577\n",
      "10 \t0.516826197999\n",
      "100 \t0.518524350888\n"
     ]
    }
   ],
   "source": [
    "for alpha in [0., 0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    linear_reg = Ridge(alpha)\n",
    "    linear_reg.fit(trainX_ex, trainY)\n",
    "    print alpha, '\\t', mean_squared_error(testY, linear_reg.predict(testX_ex))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References \n",
    "1. [matplotlib gallery](http://matplotlib.org/gallery.html)\n",
    "2. [10 minutes to pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html)\n",
    "    * almost any question about these tools is already answered on stackoverflow"
   ]
  }
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