05_Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # same as always"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFDYZsBaypoYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbee12f4e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbedef13208>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbedef80358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbeded783c8>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbedee5d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings = [3.2, 4.7, 2.1, 1.5, 5.9]\n",
    "plt.plot(ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 5 artists>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEW6AwfwPplhNRr1FF6AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbeded89940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(ratings)), ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxRBuACjmv4psaTH1PKXmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbeded95978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stds = [ 0.2,  0.3,  0.6,  0.9,  0.6]\n",
    "plt.bar(range(len(ratings)), ratings, yerr=stds)\n",
    "plt.savefig(\"./fig.pdf\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxRBuACjmv4psaTH1PKXmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbedeb67a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(ratings)), ratings, yerr=stds)\n",
    "plt.savefig(\"./fig.png\", dpi = 600, bbox_inches='tight',transparent=True, pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAr4xxswSFvCNMWaW+P91Ot7+IxsX2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbedf006eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0,10,0.01)\n",
    "\n",
    "y = x * np.sin(0.2*np.pi*x)\n",
    "plt.plot(x, y, linewidth=3)\n",
    "\n",
    "y = x * np.sin(2*np.pi*x)\n",
    "plt.plot(x, y, linewidth=3)\n",
    "\n",
    "y = 2 * x * np.sin(np.pi*x)\n",
    "plt.plot(x, y, linewidth=3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paper Ready Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>HeartBubbles</th>\n",
       "      <th>HeartLight</th>\n",
       "      <th>HeartButton</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clear and Easy \\nto Interpret</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fun and Enjoyable \\nto Use</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close and Connected \\nto Chat Partner</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Understanding State \\nof Chat Partner</th>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       HeartBubbles  HeartLight  HeartButton\n",
       "Clear and Easy \\nto Interpret                   6.0         5.0          7.0\n",
       "Fun and Enjoyable \\nto Use                      6.0         5.0          4.0\n",
       "Close and Connected \\nto Chat Partner           4.0         3.5          4.0\n",
       "Understanding State \\nof Chat Partner           4.5         4.0          4.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfM = pd.read_csv(\"./dataset_2/mean.csv\", index_col=0)\n",
    "dfM = dfM.transpose()\n",
    "dfM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>HeartBubbles</th>\n",
       "      <th>HeartLight</th>\n",
       "      <th>HeartButton</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.125000</td>\n",
       "      <td>4.375</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.030776</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.375000</td>\n",
       "      <td>3.875</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.250000</td>\n",
       "      <td>4.500</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       HeartBubbles  HeartLight  HeartButton\n",
       "count      4.000000       4.000         4.00\n",
       "mean       5.125000       4.375         4.75\n",
       "std        1.030776       0.750         1.50\n",
       "min        4.000000       3.500         4.00\n",
       "25%        4.375000       3.875         4.00\n",
       "50%        5.250000       4.500         4.00\n",
       "75%        6.000000       5.000         4.75\n",
       "max        6.000000       5.000         7.00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfM.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbedb966160>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbkAAAEHCAYAAAAzlDgtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbedbbcc518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfM.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbedbbcc4e0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAFfCAYAAABuhCaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf2KA1PJLd5tnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbedb888da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfM.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Std</th>\n",
       "      <th>HeartBubbles</th>\n",
       "      <th>HeartLight</th>\n",
       "      <th>HeartButton</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clear and Easy \\nto Interpret</th>\n",
       "      <td>0.621582</td>\n",
       "      <td>1.356801</td>\n",
       "      <td>1.311372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fun and Enjoyable \\nto Use</th>\n",
       "      <td>1.128152</td>\n",
       "      <td>1.564279</td>\n",
       "      <td>1.658312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close and Connected \\nto Chat Partner</th>\n",
       "      <td>1.831955</td>\n",
       "      <td>1.505042</td>\n",
       "      <td>1.164500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Understanding State \\nof Chat Partner</th>\n",
       "      <td>1.850471</td>\n",
       "      <td>1.443376</td>\n",
       "      <td>1.443376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Std                                    HeartBubbles  HeartLight  HeartButton\n",
       "Clear and Easy \\nto Interpret              0.621582    1.356801     1.311372\n",
       "Fun and Enjoyable \\nto Use                 1.128152    1.564279     1.658312\n",
       "Close and Connected \\nto Chat Partner      1.831955    1.505042     1.164500\n",
       "Understanding State \\nof Chat Partner      1.850471    1.443376     1.443376"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfStd = pd.read_csv(\"./dataset_2/std.csv\", index_col=0)\n",
    "dfStd = dfStd.transpose()\n",
    "dfStd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbedeffe978>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAE+CAYAAABfihBpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbede8ff3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bar_width = 0.8\n",
    "xticks = np.arange(len(dfM))\n",
    "\n",
    "dfM.plot(kind=\"bar\", width=bar_width, yerr=dfStd, figsize=(9,5))#, color=[\"b\",\"y\",\"g\"])\n",
    "\n",
    "lxticks = [u'Clear and Easy \\nto Interpret',\n",
    "           u'Fun and Enjoyable \\nto Use',\n",
    "           u'Close and Connected \\nto Chat Partner',\n",
    "           u'Understanding State \\nof Chat Partner']\n",
    "plt.xticks(xticks, tuple(lxticks),  rotation='horizontal')\n",
    "\n",
    "plt.ylim(1,9)\n",
    "plt.yticks(range(1,9), range(1,8))\n",
    "plt.ylabel('Overall Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paper Ready Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>id</th>\n",
       "      <th>group</th>\n",
       "      <th>side</th>\n",
       "      <th>condition</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  group  side  condition    time\n",
       "0   6      1     0          0   4.699\n",
       "1   7      2     0          1  12.144\n",
       "2  17      4     0          0   2.421\n",
       "3  10      5     0          1  16.224\n",
       "4  15      7     0          1  10.287"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./dataset_3/times.csv\")\n",
    "df['time'] = df['time'] / 1000\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x7fbedb53f198>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g1 = df.groupby([df.condition, df.side])\n",
    "g1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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></th>\n",
       "      <th>id</th>\n",
       "      <th>group</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <th>side</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>15.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.5976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>48.1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.6542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>20.6600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  group     time\n",
       "condition side                      \n",
       "0         0     15.8    7.0  12.5976\n",
       "          1     15.8    7.0  48.1338\n",
       "1         0     10.0    6.8   8.6542\n",
       "          1     10.0    6.8  20.6600"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEbCAYAAADKwX/cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5F+67dkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbedb6d90b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax = g1[\"time\"].mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 3.5)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD8CAYAAAB6paOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBoEkNc4gkKTG/R/1Y3Iw1nXS7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbedb1afa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "mean = g1[\"time\"].mean().tolist()\n",
    "sem = g1[\"time\"].sem().tolist()\n",
    "\n",
    "ax.set_xlim(-0.1,3.9)\n",
    "\n",
    "ax.bar([0,1], mean[:2], yerr=sem[:2])#, color=\"blue\")\n",
    "\n",
    "ax.bar([2,3], mean[2:], yerr=sem[2:]) #color=\"g\")\n",
    "\n",
    "ax.set_ylabel(\"Time [s]\")\n",
    "ax.set_xticks(np.array(range(0, 4, 1)))\n",
    "ax.set_xticklabels([\"C1-1\", \"C2-1\", \"C1-2\", \"C2-2\"])\n",
    "plt.xlim(-0.5, 3.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paper Ready Example 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = 4\n",
    "PMeans = (0.372, 0.936, 0.986, 0.389)\n",
    "PSD =    (0.042, 0.003, 0.001, 0.149)\n",
    "RMeans = (0.148, 0.871, 0.775, 0.297)\n",
    "RSDs =   (0.008, 0.009, 0.020, 0.036)\n",
    "FMeans = (0.224, 0.907, 0.870, 0.348) \n",
    "FSDs =   (0.016, 0.003, 0.008, 0.101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL08XaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbedb758b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 276 ms, sys: 236 ms, total: 512 ms\n",
      "Wall time: 241 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "w = 0.9\n",
    "width = w/N       # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind, PMeans, width, yerr=PSD)#, facecolor='y',  color='black')\n",
    "rects2 = ax.bar(ind+width, RMeans, width, yerr=RSDs)#, color='b')\n",
    "rects3 = ax.bar(ind+2*width, FMeans, width, yerr=FSDs)#,  color='r')\n",
    "\n",
    "ax.set_xlim(-0.3, 3.8)\n",
    "ax.set_ylim(0.0, 1.1)\n",
    "ax.set_ylabel('Mean Value')\n",
    "ax.set_xlabel('Classifiers')\n",
    "ax.set_xticks(ind+w*0.25)\n",
    "ax.set_xticklabels( ('TH', 'RF', 'KNN', 'DTW') )\n",
    "\n",
    "ax.legend( (rects1[0], rects2[0], rects3[0]), ('Preision', 'Recall', 'F-Score') )\n",
    "\n",
    "def autolabel(rects):\n",
    "    # attach some text labels\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x()+rect.get_width()/2., 0.01+height, height, ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "autolabel(rects3)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}