04_eda/lab_04_02_viz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations\n",
    "\n",
    "Being able to proficiently visualize your data will be important to properly understand your data. So far, we've used pyplot up to the extent that it creates plots, but we'll get more in depth with a few of them now.\n",
    "\n",
    "- The standard visualization package of python is `matplotlib`, which works fine, is very customizable, but doesn't always looks too great.\n",
    "- `Seaborn` is another package that is built on top of `matplotlib` and looks a little better.\n",
    "- `Vincent` is another package you might want to try out later.\n",
    "- We use the magic command `%matplotlib inline` so that images are embedded in the notebook.\n",
    "- See the <a href=\"http://matplotlib.org/users/pyplot_tutorial.html\" target=\"_blank\">pyplot tutorial</a> for a quick Getting Started guide.\n",
    "\n",
    "We will go over a few exercises today with some of the options out there using `matplotlib.pyplot` as well as `seaborn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The most common function of matplotlib is `plot`.\n",
    "- Type `?plt.plot` to see the doc string.\n",
    "- It takes in either a single series (i.e. plots using sequential indexes for the x axis), or two series (the first series indicating the x axis, the second series indicating the y axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "[1, 2, 5, 10, 17, 26, 37, 50, 65, 82]\n"
     ]
    }
   ],
   "source": [
    "xx = range(10)\n",
    "\n",
    "yy = [x ** 2 + 1 for x in xx]  \n",
    "\n",
    "print xx\n",
    "print yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x107c86190>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107b2e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( xx, yy, 'r:' )  # 'r' = red, and ':' = dotted line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost everything is customizable in `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GI/7Kyh0ceeQ5TJ78073Or62tAWpJS+sOwPbtK0lLy6R79+DcsrKXSU/vTa9e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\n8IuIpBgFv4hIivn/zO3GnG99cWEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1082a9a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Hello, world!\")\n",
    "plt.xlabel(\"x-axis\")\n",
    "plt.ylabel(\"y-axis\")\n",
    "plt.xlim(0, 5)\n",
    "plt.ylim(0, 70)\n",
    "plt.plot(xx, yy, 'bs-')\n",
    "plt.plot(range(6), range(6), 'y*:', markersize=10)\n",
    "\n",
    "# # I often write `f = ` on the last line to surpress the output `<matplotlib.lines.Line2D at 0x10c750390>`\n",
    "f = plt.legend(['Parabola','Star'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Rets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>0.855650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>0.455511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>0.274938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>0.315826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>0.611375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>0.430403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>0.912575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>0.694635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>0.623821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>0.862845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Rets\n",
       "2015-01-31  0.855650\n",
       "2015-02-28  0.455511\n",
       "2015-03-31  0.274938\n",
       "2015-04-30  0.315826\n",
       "2015-05-31  0.611375\n",
       "2015-06-30  0.430403\n",
       "2015-07-31  0.912575\n",
       "2015-08-31  0.694635\n",
       "2015-09-30  0.623821\n",
       "2015-10-31  0.862845"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "x = pd.DataFrame( np.random.rand(10), index=pd.date_range('2015-01-01','2015-10-31',freq='m'),columns=['Rets'] )\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x109b1be50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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De+2lJRBERDKVeOiXlpb+eLmkpISSkpI17v/ww3AA1SOPhFk3550HTz0FG22U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\n+iIiRUShLyJSRBT6IiJFRKEvIlJE/h9cKIjyFigbjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109bb2450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.cumsum().plot()\n",
    "plt.title('Cumulative Return')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Return')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>ARets</th>\n",
       "      <th>BRets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>0.810354</td>\n",
       "      <td>0.568601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>0.677169</td>\n",
       "      <td>0.370483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>0.870614</td>\n",
       "      <td>0.463935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>0.325750</td>\n",
       "      <td>0.145695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>0.957338</td>\n",
       "      <td>0.926775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>0.455868</td>\n",
       "      <td>0.357433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>0.869414</td>\n",
       "      <td>0.900460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>0.749616</td>\n",
       "      <td>0.357023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>0.200721</td>\n",
       "      <td>0.776343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>0.528388</td>\n",
       "      <td>0.241448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ARets     BRets\n",
       "2015-01-31  0.810354  0.568601\n",
       "2015-02-28  0.677169  0.370483\n",
       "2015-03-31  0.870614  0.463935\n",
       "2015-04-30  0.325750  0.145695\n",
       "2015-05-31  0.957338  0.926775\n",
       "2015-06-30  0.455868  0.357433\n",
       "2015-07-31  0.869414  0.900460\n",
       "2015-08-31  0.749616  0.357023\n",
       "2015-09-30  0.200721  0.776343\n",
       "2015-10-31  0.528388  0.241448"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.DataFrame( np.random.rand(10,2), \n",
    "                  index=pd.date_range('2015-01-01','2015-10-31',freq='m'),\n",
    "                  columns=['ARets','BRets'] )\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10a0ad250>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZvWk2UXFRTFk3hbLFytKlQRe6NuhK65qtKVyosN8hhpVA9/QTgKUi8h2Q0ttX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\nxoQRS/rGGBNGLOkbY0wY+X/ij+sjRCPUnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109eb5e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.cumsum().plot()\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Return')\n",
    "plt.title('ARet vs BRet Cumulative Return')"
   ]
  },
  {
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.214749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.410571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.268716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.249803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.819970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.770888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.641933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.092025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.716358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.672110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.665894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.221018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.593995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.885809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.081068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.337466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.675161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.419139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-1.442652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.113177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.334709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.148841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.941743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.258947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.332987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.922554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.098048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.499273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.174575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.101007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99970</th>\n",
       "      <td>0.030539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99971</th>\n",
       "      <td>-0.292711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99972</th>\n",
       "      <td>2.153135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99973</th>\n",
       "      <td>-0.942226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99974</th>\n",
       "      <td>0.586322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99975</th>\n",
       "      <td>0.620506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99976</th>\n",
       "      <td>0.597997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99977</th>\n",
       "      <td>-0.299482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99978</th>\n",
       "      <td>-1.031836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99979</th>\n",
       "      <td>0.869221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99980</th>\n",
       "      <td>-0.252347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99981</th>\n",
       "      <td>-0.789203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99982</th>\n",
       "      <td>1.303816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99983</th>\n",
       "      <td>-1.276483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99984</th>\n",
       "      <td>0.082033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99985</th>\n",
       "      <td>-2.266398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99986</th>\n",
       "      <td>0.389765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99987</th>\n",
       "      <td>0.797017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99988</th>\n",
       "      <td>-1.160177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99989</th>\n",
       "      <td>2.358920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99990</th>\n",
       "      <td>-1.053311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99991</th>\n",
       "      <td>0.154102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99992</th>\n",
       "      <td>-0.380124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99993</th>\n",
       "      <td>0.660988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99994</th>\n",
       "      <td>-0.637477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>-0.533302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>0.225316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>-2.314713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>-1.649402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>-0.758863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0\n",
       "0     -1.214749\n",
       "1     -0.410571\n",
       "2     -1.268716\n",
       "3     -0.249803\n",
       "4      1.819970\n",
       "5     -0.770888\n",
       "6      1.641933\n",
       "7     -1.092025\n",
       "8     -0.716358\n",
       "9      0.672110\n",
       "10     0.665894\n",
       "11    -1.221018\n",
       "12    -0.593995\n",
       "13     0.885809\n",
       "14     1.081068\n",
       "15    -1.337466\n",
       "16    -0.675161\n",
       "17     0.419139\n",
       "18    -1.442652\n",
       "19    -0.113177\n",
       "20     0.334709\n",
       "21     0.148841\n",
       "22    -0.941743\n",
       "23     0.258947\n",
       "24     0.332987\n",
       "25    -0.922554\n",
       "26     1.098048\n",
       "27    -0.499273\n",
       "28     0.174575\n",
       "29     2.101007\n",
       "...         ...\n",
       "99970  0.030539\n",
       "99971 -0.292711\n",
       "99972  2.153135\n",
       "99973 -0.942226\n",
       "99974  0.586322\n",
       "99975  0.620506\n",
       "99976  0.597997\n",
       "99977 -0.299482\n",
       "99978 -1.031836\n",
       "99979  0.869221\n",
       "99980 -0.252347\n",
       "99981 -0.789203\n",
       "99982  1.303816\n",
       "99983 -1.276483\n",
       "99984  0.082033\n",
       "99985 -2.266398\n",
       "99986  0.389765\n",
       "99987  0.797017\n",
       "99988 -1.160177\n",
       "99989  2.358920\n",
       "99990 -1.053311\n",
       "99991  0.154102\n",
       "99992 -0.380124\n",
       "99993  0.660988\n",
       "99994 -0.637477\n",
       "99995 -0.533302\n",
       "99996  0.225316\n",
       "99997 -2.314713\n",
       "99998 -1.649402\n",
       "99999 -0.758863\n",
       "\n",
       "[100000 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.DataFrame( np.random.randn(100000) )\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x109ec0750>]], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4vGjLfS7c/M3MasiZv5Wm+OHA8jnzt/qZLfOf0yd8zRZOVU3YzIoc+/Qh9wyvTJ6LokbV\nBWTD50Vb7nPh5m9mVkPO/K009bve3pm/VcvX+ZuZWQc3/z7knuGVyXNR1Ki6gGz4vGjLfS7c/M3M\nasiZv5XGmX+5x/bjxZz5m5lZBzf/PuSe4ZXJc1HUqLqAbPi8aMt9Ltz8zcxqyJm/lcaZf7nH9uPF\nnPmbmVkHN/8+5J7hlclzUdSouoBs+Lxoy30u3PzNzGrImb+Vxpl/ucf248Wc+ZuZWQc3/z7knuGV\nyXNR1Ki6gGz4vGjLfS56Nn9J35Z0QtKhwtgWSVOSXkk/ny/ctknShKTDkm4tjF8v6VC67ZHC+DmS\nnkjj+yVdvpB30MzMPqxn5i/pt4H3gJ0RcW0a2wz8PCK+OWPbq4DHgRuAS4AXgdUREZLGgPsjYkzS\nbuDRiNgraRS4JiJGJd0D3BkR67rU4cx/iXPmX+6x/XixM8r8I+K7wE+7/d4uY2uBXRFxMiImgaPA\nGkkXAedFxFjabidwR1q+HdiRlp8Cbu5Vk5mZnZkzyfy/LOmgpO2SBtLYxcBUYZspWu8AZo4fS+Ok\nf98AiIhTwDuSVp5BXYsm9wyvTJ6LokbVBWTD50Vb7nOxYp77fQv4o7T8x8A3gC8uSEWzGBkZYXBw\nEICBgQGGhoYYHh4G2hO9mOvNZrPU4+W83mw257V/2/T68DJfp8fti7XemvOyz4/iscs4Xs7rVfSL\n6eXJyUl6mdN1/pIGgeemM//T3SZpI0BEbEu37QU2A68D+yLiyjS+HrgxIu5L22yJiP2SVgBvRsQF\nXY7jzH+Jc+Zf7rH9eLEFv84/ZfjT7gSmrwR6Flgn6WxJVwCrgbGIOA68K2mNWh3gXuCZwj4b0vJd\nwEvzqcnMzOZuLpd67gL+Hvg1SW9I+n3g65K+L+kgcBPwVYCIGAeeBMaBPcBo4eX6KPAYMAEcjYi9\naXw7cL6kCeBBYOOC3bsFlnuGVybPRVGj6gKy4fOiLfe56Jn5R8T6LsPfnmX7rcDWLuMHgA/FRhHx\nPnB3rzrMzGzh+Lt9rDTO/Ms9th8v5u/2MTOzDm7+fcg9wyuT56KoUXUB2fB50Zb7XLj5m5nVkDN/\nK40z/3KP7ceLOfM3M7MObv59yD3DK5PnoqhRdQHZ8HnRlvtcuPmbmdWQM38rjTP/co/tx4s58zcz\nsw5u/n3IPcMrk+eiqFF1AdnwedGW+1y4+ZuZ1ZAzfyuNM/9yj+3HiznzNzOzDm7+fcg9wyuT56Ko\nUXUB2fB50Zb7XLj5m5nVkDN/K40z/3KP7ceLOfM3M7MObv59yD3DK5PnoqhRdQHZ8HnRlvtc9Pw/\nfM1saWrFbOVz3LQ0OPO30jjzr8Ox/beGnDjzNzOzDm7+fcg9wyuT56KoUXUBlqHcHyM9m7+kb0s6\nIelQYWylpBckHZH0vKSBwm2bJE1IOizp1sL49ZIOpdseKYyfI+mJNL5f0uULeQfNzOzDemb+kn4b\neA/YGRHXprGHgbci4mFJDwGfjIiNkq4CHgduAC4BXgRWR0RIGgPuj4gxSbuBRyNir6RR4JqIGJV0\nD3BnRKzrUocz/yXOmX8dju3MPydnlPlHxHeBn84Yvh3YkZZ3AHek5bXArog4GRGTwFFgjaSLgPMi\nYixtt7OwT/F3PQXc3PMemZnZGZlv5r8qIk6k5RPAqrR8MTBV2G6K1juAmePH0jjp3zcAIuIU8I6k\nlfOsa1HlnuGVyXNR1Ki6AMtQ7o+RM77OP0U6pbzPGxkZYXBwEICBgQGGhoYYHh4G2hO9mOvNZrPU\n4+W83mw257V/2/T68DJfp8fti7U+PVbW8abX01pm52sV61X0i+nlyclJepnTdf6SBoHnCpn/YWA4\nIo6nSGdfRHxK0kaAiNiWttsLbAZeT9tcmcbXAzdGxH1pmy0RsV/SCuDNiLigSw3O/Jc4Z/51OLYz\n/5wsxnX+zwIb0vIG4OnC+DpJZ0u6AlgNjEXEceBdSWvU6gD3As90+V13AS/NsyYzM5ujuVzquQv4\ne+DXJL0h6feAbcAtko4An03rRMQ48CQwDuwBRgsv10eBx4AJ4GhE7E3j24HzJU0ADwIbF+rOLbTc\nM7wyeS6KGlUXYBnK/THSM/OPiPWnuelzp9l+K7C1y/gB4Nou4+8Dd/eqw8zMFo6/28dK48y/Dsd2\n5p8Tf7ePmZl1cPPvQ+4ZXpk8F0WNqguwDOX+GHHzNzOrIWf+Vhpn/nU4tjP/nDjzNzOzDm7+fcg9\nwyuT56KoUXUBlqHcHyNu/mZmNeTM30rjzL8Ox3bmnxNn/mZm1sHNvw+5Z3hl8lwUNaouwDKU+2PE\nzd/MrIac+VtpnPnX4djO/HPizN/MzDq4+fch9wyvTJ6LokbVBViGcn+MuPmbmdWQM38rjTP/Ohzb\nmX9OnPmbmVkHN/8+5J7hlclzUdSougDLUO6PETd/M7MacuZvpXHmX4djO/PPiTN/MzPr4Obfh9wz\nvDJ5LooaVRdgGcr9MXJGzV/SpKTvS3pF0lgaWynpBUlHJD0vaaCw/SZJE5IOS7q1MH69pEPptkfO\npCYzM+vtjDJ/ST8Ero+ItwtjDwNvRcTDkh4CPhkRGyVdBTwO3ABcArwIrI6ISE8c90fEmKTdwKMR\nsXfGsZz5L3HO/OtwbGf+OVnszH/mL74d2JGWdwB3pOW1wK6IOBkRk8BRYI2ki4DzImIsbbezsI+Z\nmS2CM23+Abwo6R8kfSmNrYqIE2n5BLAqLV8MTBX2naL1DmDm+LE0np3cM7wyeS6KGlUXYBnK/TGy\n4gz3/3REvCnpAuAFSYeLN6ZIZ8HeA46MjDA4OAjAwMAAQ0NDDA8PA+2JXsz1ZrNZ6vEWa70Vv1St\nkf4dXubr9Lh9sdanx8o63vR6WsvofK9qvYp+Mb08OTlJLwt2nb+kzcB7wJeA4Yg4niKdfRHxKUkb\nASJiW9p+L7AZeD1tc2UaXw/cFBF/MOP3O/NfIM7el/txqzy2M/+cLErmL+mXJJ2Xln8ZuBU4BDwL\nbEibbQCeTsvPAusknS3pCmA1MBYRx4F3Ja1RqyvdW9jHzMwWwZlk/quA70pqAi8D34mI54FtwC2S\njgCfTetExDjwJDAO7AFGCy/lR4HHgAng6MwrfXKRe4ZXrkbVBWSkUXUBlqHc+8W8M/+I+CEw1GX8\nbeBzp9lnK7C1y/gB4Nr51mJmZv3xd/vUkDP/5X7cKo/tzD8n/m4fMzPr4Obfh9wzvHI1qi4gI42q\nC7AM5d4v3PzNzGrImX8NOfNf7set8tjO/HPizN/MzDq4+fch9wyvXI2qC8hIo+oCLEO59ws3fzOz\nGnLmX0PO/Jf7cas8tjP/nDjzNzOzDm7+fcg9wytXo+oCMtKougDLUO79ws3fzKyGnPnXkDP/5X7c\nKo/tzD8ns2X+Z/o/eZmZdajqf4rzk05/HPv0IfcMr1yNqgvISKPqAjITFfzkJ/d+4eZvZlZDzvxr\nyJn/cj9ulcf23xpy4uv8zcysg5t/H3LP8MrVqLqAjDSqLsAylHu/cPM3M6shZ/415Mx/uR+3ymM7\n88+JM38zM+uQTfOXdJukw5ImJD1UdT3d5J7hlatRdQEZaVRdgGUo936RRfOX9FHgvwO3AVcB6yVd\nWW1VH9ZsNhf090mq5GdhLOxcLG2eC/uwhe4XCy2L5g/8JnA0IiYj4iTwl8Daimv6kJ/97GeL8FuX\n6qchF2MulirPhX3Y4vSLhZNL878EeKOwPpXGzMxsEeTyxW6V/Zm+2Wxy3XXXzXn7r33ta4tYzVIy\nWXUBGZmsugAjvy+Um5ycLLeQPmVxqaek3wK2RMRtaX0T8EFEfL2wTfWFmpktMae71DOX5r8C+D/A\nzcCPgDFgfUS8VmlhZmbLVBaxT0ScknQ/8LfAR4HtbvxmZosni1f+ZmZWrlyu9llyJP0HSR9IWll1\nLVWR9N8kvSbpoKT/LekTVddUpqXwwcSySLpM0j5JP5D0qqSvVF1TlSR9VNIrkp6rupbTcfOfB0mX\nAbcAr1ddS8WeB66OiF8HjgCbKq6nNEvlg4klOgl8NSKuBn4L+Pc1n48HgHFy/W/GcPOfr28C/7nq\nIqoWES9ExAdp9WXg0irrKdmS+GBiWSLieEQ00/J7wGvAxdVWVQ1JlwK/AzxG65vusuTm3ydJa4Gp\niPh+1bVk5veB3VUXUSJ/MPE0JA0C19F6QVBHfwL8J+CDXhtWKYurfXIj6QXgwi43/SGtaOPW4ual\nFFWRWebiv0TEc2mbPwT+b0Q8Xmpx1cr27XyVJJ0L/BXwQHoHUCuS/hXw44h4RdJw1fXMxs2/i4i4\npdu4pGuAK4CD6dOElwIHJP1mRPy4xBJLc7q5mCZphNZb3JtLKSgfx4DLCuuX0Xr1X1uSzgKeAv4i\nIp6uup6K/Evgdkm/A3wM+BVJOyPidyuu60N8qecZkPRD4PqIeLvqWqog6TbgG8BNEfFW1fWUyR9M\n7KTWq6EdwE8i4qtV15MDSTcB/zEi/nXVtXTjzP/M1P2Z88+Ac4EX0mVtf151QWWJiFPA9AcTx4En\n6tr4k08DXwA+k86FV9KLg7rLtkf4lb+ZWQ35lb+ZWQ25+ZuZ1ZCbv5lZDbn5m5nVkJu/mVkNufmb\nmdWQm7+ZWQ25+ZuZ1dD/A6delU6u2WpEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a051990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram is a great way to get a distribution of a series. \n",
    "# I.e. if you don't know how a series is distributed, use histogram!\n",
    "# Use ?plt.hist() or ?x.hist() to get more information, or just hit shift-tab while you're typing out the function\n",
    "x.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>ARets</th>\n",
       "      <th>BRets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>0.524501</td>\n",
       "      <td>0.345865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>0.940621</td>\n",
       "      <td>0.673452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>0.774593</td>\n",
       "      <td>0.279191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>0.159164</td>\n",
       "      <td>0.681343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>0.644236</td>\n",
       "      <td>0.615781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>0.397424</td>\n",
       "      <td>0.624614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>0.928215</td>\n",
       "      <td>0.928342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>0.898399</td>\n",
       "      <td>0.032741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>0.521137</td>\n",
       "      <td>0.559238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>0.668097</td>\n",
       "      <td>0.752984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ARets     BRets\n",
       "2015-01-31  0.524501  0.345865\n",
       "2015-02-28  0.940621  0.673452\n",
       "2015-03-31  0.774593  0.279191\n",
       "2015-04-30  0.159164  0.681343\n",
       "2015-05-31  0.644236  0.615781\n",
       "2015-06-30  0.397424  0.624614\n",
       "2015-07-31  0.928215  0.928342\n",
       "2015-08-31  0.898399  0.032741\n",
       "2015-09-30  0.521137  0.559238\n",
       "2015-10-31  0.668097  0.752984"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #Lets go back to a bivariate data frame\n",
    "x = pd.DataFrame( np.random.rand(10,2), \n",
    "                  index=pd.date_range('2015-01-01','2015-10-31',freq='m'),\n",
    "                  columns=['ARets','BRets'] )\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10a917850>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tno4DIH3tw6JFj8dZlkib6fCUiIhknWa5FQkwffpUli2bQm1tejk90WJVvEWJxECHp0QC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\nSDCFhoiIBPv/rSj74IpzojIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a917790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Let's plot 'arets' and 'brets' as points on a plot\n",
    "plt.scatter( x['ARets'], x['BRets'] )\n",
    "plt.xlabel('ARet')\n",
    "plt.ylabel('BRet')\n",
    "plt.legend(['ABRet Points'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seaborn\n",
    "\n",
    "Let's see how good the graphs look with `seaborn`. \n",
    "\n",
    "`import seaborn as sb`\n",
    "\n",
    "Once you've imported seaborn, you can just call pyplot functions as is and you'll get prettier plots. In addition, the seaborn package itself has additional plots you can call. Let's rerun the scatter plot to see how much prettier it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10c406e10>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c338f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Let's plot 'arets' and 'brets' as points on a plot\n",
    "plt.scatter( x['ARets'], x['BRets'] )\n",
    "# plt.xlabel('ARet')\n",
    "# plt.ylabel('BRet')\n",
    "# plt.legend(['ABRet Points'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c615c10>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wNhteGagN/SMUVHSSEKkjMcr7hrvbm+2cuqRGnFML7sFsNfNe1UGUkpI7knfO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\nwChRE0IIIQJGiZoQQggRsP8P7TLGevaVXSwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c615450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about for histogram?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# x = pd.DataFrame( np.random.randn(100000) )\n",
    "# x.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll try using seaborn more throughout the class, and will have an example later today."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anscombe's Quartet\n",
    "Source: [Wikipedia](https://en.wikipedia.org/wiki/Anscombe's_quartet)\n",
    "\n",
    "To start off with, let's take a look at Anscombe's Quartet. It is a collection of four different datasets, where each dataset has an $x$ and $y$ dimension.\n",
    "\n",
    "Their basic statistics looks very similar, but they're not quite identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# import numpy as np\n",
    "# from matplotlib import pyplot as plt\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame({'x1': [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0],\n",
    "     'y1': [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68],\n",
    "     'x2': [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0,],\n",
    "     'y2': [9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74],\n",
    "     'x3': [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0,],\n",
    "     'y3': [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73],\n",
    "     'x4': [8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 19.0, 8.0, 8.0, 8.0],\n",
    "     'y4': [6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89]})\n",
    "data # This dataframe contains anscombe's quartet (i.e. (x1,y1), (x2,y2), (x3,y3), (x4,y4) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "\n",
    "**1) For each of the four datasets (i.e., for $(x_1, y_1)$, for $(x_2, y_2)$, etc), compute some basic statistics:**\n",
    "\n",
    "For $x_i$ and $y_i$, generate:\n",
    "- the mean and the standard deviation\n",
    "- the correlation between $x_i$ and $y_i$. Hint: Use np.corrcoef or the Pandas dataframe function corr()\n",
    "\n",
    "Hint: Create labels of $x_i$ and $y_i$ to be able to index into the dataframe for each $i$. This way, you can subset the columns in the `data` dataframe, and call mean(), std()\n",
    "\n",
    "Hint2: Pandas dataframe calls to mean(), std(), corr(), etc. can be tuple unpacked\n",
    "\n",
    "```\n",
    "df = pd.DataFrame([[0,1],[2,3]], index = [0,1],columns=['A','B'])\n",
    "asum, bsum = df.sum()\n",
    "print asum, bsum\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in xrange(1,5):\n",
    "    x,y = \"x\" + str(i), \"y\" + str(i)\n",
    "    # Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These data points must be exactly the same..? \n",
    "\n",
    "**2) Generate a scatterplot for each set of $x_i$ and $y_i$.**\n",
    "\n",
    "Hint: Use the \"plt.scatter\" function, passing in array of x, and the array of y you would like to scatterplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in xrange(1,5):\n",
    "    x,y = \"x\"+str(i), \"y\"+str(i)\n",
    "    plt.figure(figsize=(18,4))\n",
    "    plt.subplot(2,2,i-1)\n",
    "    # Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3) Seaborn is a visual package that is built on top of Matplotlib Pyplot. It makes plots look a lot nicer, and has some additional plotting capabilities (for instance heat maps). **\n",
    "\n",
    "If you finish the exercise early, go ahead and install `seaborn`.\n",
    "\n",
    "Go to your terminal, and type in `pip install seaborn`. Afterwards, try importing seaborn ala:\n",
    "\n",
    "`import seaborn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importance of Good Visualization\n",
    "\n",
    "We've seen how important it is to visualize our data. Even when data aggregate statistics are the same, they could be vastly different! Now, let's try plotting out some different plots to get ourselves familiar with the various plot types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4) Plot the function $f(x) = x^3 - 2x^2 + 4$ on the domain $[-5, 5]$. You can plot at each point by using `range`, or use np.lin_space to help generate points**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get some arbitrary dataset (see below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np  # import numpy -- we will cover this later\n",
    "import pandas as pd\n",
    "N = 1000  # number of dots\n",
    "x = np.random.random(N)  # get N random values between 0 and 1\n",
    "df = pd.DataFrame(dict(\n",
    "        A=x,\n",
    "        B=func(x),\n",
    "        C=func(x) / func(x - 1)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scatterplot A against B, A against C, and B against C**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Scatter of A vs B here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Scatter of A vs C here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Scatter of B vs C here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot A against B, and make the size of the dots correspond with 100 * C. Hint: plt.scatter can take in an array for the `s` argument for indicating size**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot B against C, and make the _color_ of the dots correspond with A. (This should result in a grey scale.)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Further reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- <b>Pandas</b>:\n",
    "<a href=\"http://pandas.pydata.org/pandas-docs/stable/10min.html\" target=\"_blank\">10 Minutes to Pandas</a> and\n",
    "<a href=\"http://pandas.pydata.org/pandas-docs/stable/tutorials.html\" target=\"_blank\">tutorials</a>\n",
    "of the official documentation<br>\n",
    "Also recommended is the book <i>Python for Data Analysis</i>, O'Reilly Media.\n",
    "- <b>Matplotlib pyplot</b>:\n",
    "<a href=\"http://matplotlib.org/users/pyplot_tutorial.html\" target=\"_blank\">tutorial</a> \n",
    "of the official documentation\n",
    "- <b>Seaborn</b>:\n",
    "<a href=\"http://stanford.edu/~mwaskom/software/seaborn/\" target=\"_blank\">website</a>\n",
    "- <b>Vincent</b>:\n",
    "<a href=\"https://vincent.readthedocs.org/en/latest/\" target=\"_blank\">website</a>"
   ]
  }
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