Visualization Examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n",
      "14\n"
     ]
    }
   ],
   "source": [
    "x = [0.9, 2.0, 2.7, 3.6, 4.8, 6.0, 6.3, 6.7, 7, 8.7, 10.9,  12.5, 13.4, 14.6]\n",
    "y = [0.5, 0.75, 1.1, 3.6, 7.3, 15.7, 19, 28.6, 32.6, 32.8,  32.1, 33.1, 32.9, 33]\n",
    "\n",
    "print(len(x))\n",
    "print(len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9 ,  0.5\n",
      "2.0 ,  0.75\n",
      "2.7 ,  1.1\n",
      "3.6 ,  3.6\n",
      "4.8 ,  7.3\n",
      "6.0 ,  15.7\n",
      "6.3 ,  19\n",
      "6.7 ,  28.6\n",
      "7 ,  32.6\n",
      "8.7 ,  32.8\n",
      "10.9 ,  32.1\n",
      "12.5 ,  33.1\n",
      "13.4 ,  32.9\n",
      "14.6 ,  33\n"
     ]
    }
   ],
   "source": [
    "for a,b in zip(x,y):\n",
    "    print(a, \", \" ,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p5g0AOFdXfrIYAFA/igAAMueIhg/IaTnbs5KeS51jFeslvZA6RAM6La/UeZnJW75Oy5wi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\nZI4iAIDMUQQAkDmKAAAyRxEAQOb+H384SltSGwP6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe56496aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axis = plt.subplots()\n",
    "# Grid lines, Xticks, Xlabel, Ylabel\n",
    "\n",
    "#axis.yaxis.grid(True)\n",
    "#axis.set_title('CO2 Emissions vs. GDP \\(per capita\\)',fontsize=10)\n",
    "axis.set_xlabel('x',fontsize=10)\n",
    "axis.set_ylabel('y',fontsize=10)\n",
    "\n",
    "axis.scatter(x, y)\n",
    "plt.show()\n",
    "fig.savefig('vis_example.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.15 ,  4.18701223172 , 6.5\n",
      "19.5035714286 ,  13.6389733627 , 23.8\n"
     ]
    }
   ],
   "source": [
    "x_np = np.array(x)\n",
    "y_np = np.array(y)\n",
    "\n",
    "print(x_np.mean(), \", \", x_np.std(), \",\", np.median(x_np))\n",
    "print(y_np.mean(), \", \", y_np.std(), \",\", np.median(y_np))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.88305172],\n",
       "       [ 0.88305172,  1.        ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(x_np, y_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Midterm</th>\n",
       "      <th>Final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.46750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.62500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.53750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.92750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.63750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.63125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.35625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.63125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.72500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.51250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.48750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.92</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.88750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.97500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.75625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.79375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.74000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.48125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Midterm    Final\n",
       "0      0.42  0.46750\n",
       "1      0.54  0.62500\n",
       "2      0.42  0.53750\n",
       "3      0.86  0.92750\n",
       "4      0.60  0.63750\n",
       "5      0.51  0.63125\n",
       "6      0.30  0.35625\n",
       "7      0.61  0.65000\n",
       "8      0.58  0.63125\n",
       "9      0.76  0.72500\n",
       "10     0.66  0.51250\n",
       "11     0.63  0.48750\n",
       "12     0.92  1.00000\n",
       "13     0.86  0.88750\n",
       "14     0.91  0.97500\n",
       "15     0.82  0.75625\n",
       "16     0.80  0.85000\n",
       "17     0.69  0.79375\n",
       "18     0.62  0.74000\n",
       "19     0.58  0.48125\n",
       "20     0.86  0.00000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.read_csv(\"virtual_grades.csv\")\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x2_np = np.array(frame['Midterm'])\n",
    "y2_np = np.array(frame['Final'])\n",
    "\n",
    "#x2_np = np.array(frame['Midterm'].values)\n",
    "#frame.columns.values\n",
    "\n",
    "#y2_np = np.array(frame['Final'].values)\n",
    "#y2_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEKCAYAAAAMzhLIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5646dd080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axis = plt.subplots()\n",
    "# Grid lines, Xticks, Xlabel, Ylabel\n",
    "\n",
    "#axis.yaxis.grid(True)\n",
    "#axis.set_title('CO2 Emissions vs. GDP \\(per capita\\)',fontsize=10)\n",
    "axis.set_xlabel('x',fontsize=10)\n",
    "axis.set_ylabel('y',fontsize=10)\n",
    "plt.axis([0,1.0, -0.02, 1.02])\n",
    "\n",
    "axis.scatter(x2_np,y2_np )\n",
    "plt.show()\n",
    "fig.savefig('vis_example2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.       ,  0.8925496],\n",
       "       [ 0.8925496,  1.       ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(x2_np[:20], y2_np[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JUiODQpLUyKCQJDUyKCRJjQwKSVIjg0KS1MigkCQ1MigkSY0MCklSo1aDIskNSU4lOZ3k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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5646cc5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axis = plt.subplots()\n",
    "# Grid lines, Xticks, Xlabel, Ylabel\n",
    "\n",
    "#axis.xaxis.grid(True)\n",
    "#axis.yaxis.grid(True)\n",
    "#axis.set_title('Midterm vs. Final Exam',fontsize=10)\n",
    "axis.set_xlabel('Midterm',fontsize=10)\n",
    "axis.set_ylabel('Final',fontsize=10)\n",
    "plt.axis([0,1.0, -0.02, 1.02])\n",
    "\n",
    "axis.scatter(x2_np,y2_np )\n",
    "plt.show()\n",
    "fig.savefig('vis_example2_better.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roles of graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hISHKmzevKlWqpDlz5qRp97///U/lypWTj4+PwsPD9d133zmsHzNmjIKCglS4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\nB08yMjIyMjIyMjXg/wEIJHPaddCUmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe561d43390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('fivethirtyeight') \n",
    "plt.style.use('default') \n",
    "\n",
    "num_courses = 5\n",
    "n_groups = 4\n",
    "means_SI = (24,14,25,16,20)\n",
    "means_PI = (10,11,6,3,7)\n",
    "courses = (\"CS1*\", \"CS1.5\",\"Theory*\",\"Arch*\",\"Average*\")\n",
    " \n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(num_courses)\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, means_SI, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='b',\n",
    "                 label='Standard Instruction')\n",
    " \n",
    "rects2 = plt.bar(index + bar_width, means_PI, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='g',\n",
    "                 label='Peer Instruction')\n",
    " \n",
    "plt.xlabel('* denotes statistically significant (p<0.05)',fontsize=12,fontstyle='italic')\n",
    "plt.ylabel('Failure Rate',fontsize=14)\n",
    "#plt.title('Failure Rate by Pedagogy')\n",
    "plt.xticks(index + bar_width, courses, fontsize = 14)\n",
    "\n",
    "vals = ax.get_yticks()\n",
    "ax.set_yticklabels(['{:3.0f}%'.format(x) for x in vals])\n",
    "\n",
    "plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "           ncol=2, mode=\"expand\", borderaxespad=0., fontsize=14,frameon=False)\n",
    "#plt.legend()\n",
    "\n",
    "x1,x2,y1,y2 = plt.axis()\n",
    "\n",
    "plt.axis((x1,x2,y1,y2))\n",
    "\n",
    "\n",
    "plt.plot([3.66, 3.66], [0, 29], 'k-', lw=2)\n",
    "\n",
    "\n",
    "    \n",
    "#plt.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig('vis_presenting.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Honesty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b9dhjj2n37t1lHvOvPU3Z2dkKDQ3V4cOH5efn58inBwAAqkhOTo5CQkKUlZUl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe561ca6b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('fivethirtyeight') \n",
    "plt.style.use('default') \n",
    "\n",
    "quarterly_profits_16 = (8.25,8.28,8.32,8.42)\n",
    "quarterly_profits_15 = (7.9,8.0,8.1,8.7)\n",
    "\n",
    "quarters = (\"Q1\", \"Q2\",\"Q3\",\"Q4\")\n",
    " \n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(len(quarters))\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, quarterly_profits_16, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='b',\n",
    "                 label='Standard Instruction')\n",
    " \n",
    "#rects2 = plt.bar(index + bar_width, means_PI, bar_width,\n",
    "#                 alpha=opacity,\n",
    "#                 color='g',\n",
    "#                 label='Peer Instruction')\n",
    " \n",
    "#plt.xlabel('* denotes statistically significant (p<0.05)',fontsize=12,fontstyle='italic')\n",
    "plt.ylabel('Revenue (Million USD)',fontsize=14)\n",
    "plt.title('Profits Surge in 4th Quarter', fontsize=20, color='g')\n",
    "plt.xticks(index + bar_width, quarters, fontsize = 14)\n",
    "\n",
    "plt.yticks(fontsize = 8)\n",
    "\n",
    "#vals = ax.get_yticks()\n",
    "#ax.set_yticklabels(['{:3.0f}%'.format(x) for x in vals])\n",
    "\n",
    "#plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "#           ncol=2, mode=\"expand\", borderaxespad=0., fontsize=14,frameon=False)\n",
    "#plt.legend()\n",
    "\n",
    "x1,x2,y1,y2 = plt.axis()\n",
    "\n",
    "plt.axis((x1,x2,8.2,8.45))\n",
    "\n",
    "\n",
    "    \n",
    "#plt.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig('vis_misleading.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Llp+nn9JN6Xpty2ua3mW6AjsEmnscKntU1oqBK1TSqaSSUpO0/tT6HPue02eOXmn3iuqU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\n0AMAAEyB0AMAAEyB0AMAAEzhvwJTaEqQmn+PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f03fcd392e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('fivethirtyeight') \n",
    "plt.style.use('default') \n",
    "\n",
    "quarterly_profits_16 = (8.25,8.28,8.32,8.42)\n",
    "quarterly_profits_15 = (7.9,8.0,8.1,8.7)\n",
    "\n",
    "quarters = (\"Q1\", \"Q2\",\"Q3\",\"Q4\")\n",
    " \n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(len(quarters))\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, quarterly_profits_16, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='b',\n",
    "                 label='Standard Instruction')\n",
    " \n",
    "#rects2 = plt.bar(index + bar_width, means_PI, bar_width,\n",
    "#                 alpha=opacity,\n",
    "#                 color='g',\n",
    "#                 label='Peer Instruction')\n",
    " \n",
    "plt.xlabel('2016',fontsize=12,fontstyle='italic')\n",
    "plt.ylabel('Revenue (Million USD)',fontsize=14)\n",
    "plt.title('Profits Remain Steady in 2016', fontsize=18, color='g')\n",
    "plt.xticks(index + bar_width, quarters, fontsize = 14)\n",
    "\n",
    "#plt.yticks(fontsize = 8)\n",
    "\n",
    "#vals = ax.get_yticks()\n",
    "#ax.set_yticklabels(['{:3.0f}%'.format(x) for x in vals])\n",
    "\n",
    "#plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "#           ncol=2, mode=\"expand\", borderaxespad=0., fontsize=14,frameon=False)\n",
    "#plt.legend()\n",
    "\n",
    "x1,x2,y1,y2 = plt.axis()\n",
    "\n",
    "#plt.axis((x1,x2,8.2,8.45))\n",
    "\n",
    "\n",
    "#plt.plot([3.66, 3.66], [0, 29], 'k-', lw=2)\n",
    "\n",
    "\n",
    "    \n",
    "#plt.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig('vis_better1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iNDS03LCTm5uL3Nx/5yIoKipCamoq6tatW2NuxkhERPRfJ4oinjx5AltbW+jo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\n7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe561c22b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('fivethirtyeight') \n",
    "plt.style.use('default') \n",
    "\n",
    "quarterly_profits_16 = (8.25,8.28,8.32,8.42)\n",
    "quarterly_profits_15 = (7.9,8.0,8.3,8.9)\n",
    "\n",
    "quarters = (\"Q1\", \"Q2\",\"Q3\",\"Q4\")\n",
    " \n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(len(quarters))\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, quarterly_profits_15, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='b',\n",
    "                 label='2015')\n",
    " \n",
    "rects2 = plt.bar(index + bar_width, quarterly_profits_16, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='g',\n",
    "                 label='2016')\n",
    " \n",
    "#plt.xlabel('2017',fontsize=12,fontstyle='italic')\n",
    "plt.ylabel('Revenue (Million USD)',fontsize=14)\n",
    "plt.title('Smaller Profit Growth in Q4 for 2016', fontsize=16, color='g')\n",
    "plt.xticks(index + bar_width, quarters, fontsize = 14)\n",
    "\n",
    "#plt.yticks(fontsize = 8)\n",
    "\n",
    "#vals = ax.get_yticks()\n",
    "#ax.set_yticklabels(['{:3.0f}%'.format(x) for x in vals])\n",
    "\n",
    "#plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "#           ncol=2, mode=\"expand\", borderaxespad=0., fontsize=14,frameon=False)\n",
    "plt.legend(ncol=2,loc=9)\n",
    "\n",
    "x1,x2,y1,y2 = plt.axis()\n",
    "plt.axis((x1,x2,y1,10.0))\n",
    "\n",
    "\n",
    "#plt.plot([3.66, 3.66], [0, 29], 'k-', lw=2)\n",
    "\n",
    "\n",
    "    \n",
    "#plt.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig('vis_better2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAADYCYIdAACAnSDYAQAA2AmCHQAAgJ0g2AEAANgJgh0AAICdINgBAADYCYId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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5646894a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('fivethirtyeight') \n",
    "plt.style.use('default') \n",
    "\n",
    "cpi_spec = (2.1,2.3,1.9,2.8)\n",
    "#quarterly_profits_15 = (7.9,8.0,8.1,8.7)\n",
    "\n",
    "procs = (\"x21j\", \"x22j\",\"x22k\",\"x23i\")\n",
    " \n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(len(procs))\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, cpi_spec, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='b',\n",
    "                 label='spec')\n",
    " \n",
    "#rects2 = plt.bar(index + bar_width, means_PI, bar_width,\n",
    "#                 alpha=opacity,\n",
    "#                 color='g',\n",
    "#                 label='Peer Instruction')\n",
    " \n",
    "plt.xlabel('Model',fontsize=12,fontstyle='italic')\n",
    "plt.ylabel('IPC (Ave. SPEC 2006)',fontsize=14)\n",
    "plt.title('Performance by CPU Model', fontsize=18, color='g')\n",
    "#plt.xticks(index + bar_width, procs, fontsize = 14)\n",
    "plt.xticks(index, procs, fontsize = 14)\n",
    "\n",
    "#plt.yticks(fontsize = 8)\n",
    "\n",
    "#vals = ax.get_yticks()\n",
    "#ax.set_yticklabels(['{:3.0f}%'.format(x) for x in vals])\n",
    "\n",
    "#plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "#           ncol=2, mode=\"expand\", borderaxespad=0., fontsize=14,frameon=False)\n",
    "#plt.legend()\n",
    "\n",
    "#x1,x2,y1,y2 = plt.axis()\n",
    "\n",
    "#plt.axis((x1,x2,8.2,8.45))\n",
    "\n",
    "\n",
    "#plt.plot([3.66, 3.66], [0, 29], 'k-', lw=2)\n",
    "\n",
    "\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig('vis_accessible.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}