Introduction to Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Data Visualization is an important and exciting aspect of data science. It reveals information we otherwise wouldn't have noticed. It allows us to showcase the work we've done through visualizations, which can be stagnant or interactive. \n",
    "\n",
    "### Python Modules\n",
    "\n",
    "[matplotlib](http://matplotlib.org/) is a 2D python plotting library which allows you to generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.\n",
    "\n",
    "[bokeh](http://bokeh.pydata.org/en/latest/) is an interactive visualization library for modern web browsers presentation. \n",
    "\n",
    "[seaborn](http://seaborn.pydata.org/introduction.html#introduction) is a library for making statistical graphics in Python. It's built on top of matplotlib and is tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. \n",
    "\n",
    "[ggplot](http://ggplot.yhathq.com/) is a plotting system built for making profressional-looking plots quickly with minimal code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib\n",
    "\n",
    "`matplotlib.pyplot` is a collection of functions that make matplotlib work similar to matlab. Each pyplot function makes some change to a figure. In `matplotlib.pyplot` various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes. \n",
    "\n",
    "\n",
    "### Basic Plots\n",
    "\n",
    "In this section, we'll overview the basic plot types: line plots, scatter plots, and histograms.\n",
    "\n",
    "#### Line Plots \n",
    "\n",
    "Line graphs are plots where a line is drawn to indicate a relationship between a particular set of x and y values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to plot anything, we need to provide the data points, so we declare those as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = [1, 2, 3, 4, 5]\n",
    "y = [1, 4, 9, 16, 25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `matplotlib`, we can plot a line between plot x and y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10db1df28>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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SfhpbOrhs9hg+f71hRJxe1sOB/pdFQlCnz8eLW8t4+d1jREdF8PnrDUvnjyciQmtvDxcq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\npPIWEQlB/x+OL2IwY4UOPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d55b7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter Plots\n",
    "\n",
    "Alternatively, you might want to plot quantities with 2 positions as data points. To do this, you first have to import the needed libraries, as always. We'll be using the same data from before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = [1, 2, 3, 4, 5]\n",
    "y = [1, 4, 9, 16, 25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot it with the `plt.plot()` method. Note that the `o` denotes the type of graph we're plotting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1051eb438>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7Jd1u+5wqf+/gF23//apjilivqrkKWi9FRCn7flHSk6r83YiVWvrzuNkK/Zik38quFN8k\n6a2IuFB0KNs/sXze0PY+Vda15SWQzfmwpLMR8eA6h7V9zfLkKmLNbPfZ7s22eyR9VNL3Vh12TNLB\nbPsTkp6N7GpWkblWnWe9XZXrEi0VEeMRcX1EDKhywfPZiPjNVYe1fb3y5CpivWy/1/a1y9uSPiZp\n9Z1xLf157Kg/Em37cVXufthu+zVJn1XlApEi4kuqfG7MbZJekfRfkn67Q3J9QtLv2b4sqSzpjlb/\nR53ZL+kuSXPZ+VdJekDSDSuyFbFmeXIVsWY7JR115c8nvkfS1yLiKdt/KmkmIo6p8j+iv7P9iioX\nwu9ocaa8uf7A9u2SLme57m5DrjV1wHrlyVXEeu2Q9GT2OmWbpMci4tu2f1dqz88j7xQFgERstlMu\nAIB1UOgAkAgKHQASQaEDQCIodABIBIUOAImg0AEgERQ6ACTifwEcr4nJpMiBMgAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1039c3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms\n",
    "\n",
    "Histograms are very often used in science applications and it's highly likely that you will need to plot them at some point. They are very useful to plot distributions. As before, we'll use `numpy` and `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll make the data to plot. We're going to make a normal distribution with 1000 points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.normal(5.0, 3.0, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we actually make that histogram of the data array and attach a label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   1.,    4.,   36.,  113.,  200.,  271.,  220.,  107.,   35.,   13.]),\n",
       " array([ -6.282258  ,  -4.23643692,  -2.19061584,  -0.14479477,\n",
       "          1.90102631,   3.94684738,   5.99266846,   8.03848953,\n",
       "         10.08431061,  12.13013169,  14.17595276]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQy9JjTP0ktQ4Qy9JjTP0ktS4/weDHiiBQi/YAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105145b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customization\n",
    "\n",
    "The default customization for matplotlib isn't very appealing or even helpful in data visualization tasks. \n",
    "\n",
    "### Colors\n",
    "\n",
    "When there are multiple data points or objects, they have to be able to be differentiated between one another. An easy way to do that is by using different marker styles and colors. You can do this by editing the third parameter to include a letter that indicates the color, such as: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1053ac390>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N0xtTJAutWhVWSLz4Yjj2WJg7N4ydi0SSzGnE34BNH2LYH5jq7nsBUxPfi9Qe770Hhx4K\nTz4ZbuWfNAkKCmKnklqu0kJ39+nAt5ts7gyMTLweCXRJcS6R7PX3v4cy/+67cBv/jTdCnTqxU4lU\newx9Z3dflHi9GNg5RXlEsteaNeGW/R49wuyVOXOgdevYqUT+rcZXbtzdAa/ofTPrZWbFZlZcUlJS\n092JxPHxx9CyJTzySHggxZQp0KRJ7FQiv1DdQl9iZk0AEn8ureiD7j7M3QvdvbBAY4ySi0aPDlMS\nv/oqjJXfeitsqZusJftUt9CfBXokXvcAxqcmjkgWWbs23LLfvTvsv3+YxdK+fexUIhVKZtrik8Cb\nwN5m9pWZ9QRuA9qa2XzghMT3Ivnjs8+gVSu4/37o1w9efRWaNYudSmSzKv13o7ufWcFbbVKcRSQ7\njB8fLnwCPPMMdNEkLskNup1N5Cfr14ez8S5dYM89w1osKnPJIbqyIwLw5Zdwxhnw5pvQuzfcdRds\nvXXsVCJVokKX2m3DBnjssXBmvnZtuPOze/fYqUSqRUMuUju5hymIBx0E558Pe+wBxcUqc8lpKnSp\nfd56K9zh2bEjrF4d5pnPnAl77x07mUiNqNCl9pg/H7p1g8MPD4trDRkC778ftumJQpIHNIYu+W/J\nkrAi4rBh4ULnTTeFMfPttoudTCSlVOiSv77/Hu68E+64I1zw7NUrrIy4yy6xk4mkhQpd8s/69eFs\n/JZbYOnS8JzPW2+FvfaKnUwkrVTokj/c4emn4frr4ZNPwlOEJkzQg5ql1tBFUckP06aFi51nnAH1\n6sFzz4VtKnOpRVToktveeQc6dAjTEBcvhr/9LayK2KGDZq5IraNCl9y0YEFYQKtFC5gxAwYPDg+h\n6NFDj4OTWktj6JJbvv0W/vznMIcc4OqroX9/aNgwbi6RLKBCl9ywZg3cdx8MGgQrV4bb9QcM0Brl\nIhvRkItktw0bYMSIMOWwf384+ugwbj5ihMpcZBMqdMlO7mHK4YEHQs+e0LQpvPJK2LbffrHTiWQl\nFbpknxkzwhzyTp1g3ToYMyasU37ssbGTiWQ1Fbpkj48+gq5d4YgjwoyVoUPDIlpdu2oKokgSdFFU\n4lu0KFzgHD4c6tcPt+z/4Q/QoEHsZCI5RYUu8axcGeaP33VXGFrp3RtuuAH+679iJxPJSSp0ybx1\n6+DBB+FPf4Jly8Lt+rfeCr/+dexkIjlNY+iSOWVlMGoU/M//QN++cMAB8PbbYZvKXKTGVOiSGVOn\nhoWyzjwzPFjihRdgyhQoLIydTCRvqNAlvebOhRNPhBNOCMMrjz4Ks2eHbZq5IpJSKnRJj88/h3PO\ngYMOguLicOHzww/Dti30104kHXRRVFJr2bJwgfOBB0Jx9+8P//u/sOOOsZOJ5D0VuqTG6tVw771w\n222wahVccEGYW77rrrGTidQaKnSpmdLS8FCJm26ChQvD7fqDBsG++8ZOJlLraDBTqscdxo8PUw8v\nugh+9St47bWwTWUuEoUKXarujTfCMrZduoS55c88E7YddVTsZCK1mgpdkvfBB6HEjzoKPv0UHnoI\n5s0L2zQFUSS6Go2hm9nnwPfABqDU3XWXSD5auBBuvhkeeQS23RYGDoQrrgivRSRrpOKi6PHuviwF\n/zmSbVasgNtvh7vvDhc/L7ssLJ7VuHHsZCJSDs1ykf+0dm1Yi3zgQPjmGzjrrPB6991jJxORzajp\nGLoDL5nZLDPrlYpAElFZGTz+OOyzT1iP/OCDw236jz+uMhfJATUt9KPc/WCgPXCpmR2z6QfMrJeZ\nFZtZcUlJSQ13J2nz0ktwyCHh1vyGDcP3L70Ubt0XkZxQo0J3968Tfy4FngEOK+czw9y90N0LCwoK\narI7SYdZs6Bt27BY1vLl4Wy8uDhsE5GcUu1CN7NtzWy7n14D7YB5qQomafbpp2Ep28JCmDMH7rkn\nLJ511llaPEskR9XkoujOwDMW5h9vCTzh7i+kJJWkT0lJeFLQgw/CllvC9dfD1VfDDjvETiYiNVTt\nQnf3T4EDU5hF0umHH8IStoMHh4W0evYM66/893/HTiYiKaJpi/lu/fpwQ9CAAbB4cbirc9CgMJNF\nRPKKCj1fucPYsXDddfDxx9CqFRQVwZFHxk4mImmiq1/5ZtUqGD06FPdpp0GdOmEFxNdeU5mL5Dmd\noeeD5cthwgQYMwZefDHc6dm0KQwfDj16hIufIpL39Jueq0pKwpl3URFMnRrGynfdFS6+GLp2DUMs\nderETikiGaRCzyULF4a1x4uK4NVXw636u+8OffuGEj/sMM0hF6nFVOjZ7osvwsXNoiL4xz/Cxc59\n9oFrrw0l3qKF1iIXEUCFnp3mzw8FXlQUbsOH8Ki3m28OFzr1iDcRKYcKPRu4w/vvh4uaRUXw7rth\n+6GHwm23hTPxPfeMm1FEsp4KPRb3sIbKT2fiH30Uhk5atQoPlPjtb2G33WKnFJEcokLPpLIyeOut\ncCY+dix89lm4iHnccXD55XDqqdCkSeyUIpKjVOjptmEDvP56OAsfOxa+/hrq1oUTTggLY3XurEe6\niUhKqNDTYf16mDYtlPi4cbB0KdSrF9YcHzQITjkFdtwxdkoRyTMq9FT58UeYPDmU+LPPwnffwbbb\nQseO4aJmhw7QoEHslCKSx1ToNfHDD/DCC6HEJ06E778P64qfckqYXtiuHdSvHzuliNQSKvSqWrky\nlHdRETz/PKxZAzvtBN26hTPxNm1gq61ipxSRWkiFnoxvvw3DKEVF4cHJ69bBLrvABReEEj/mGC2A\nJSLRqYUqsmRJuKBZVBQucJaWhnnhvXuHEj/ySK2bIiJZRYW+sa+++nndlNdfD/PG99wT+vULJV5Y\nqHVTRCRrqdA/++znuzVnzAjb9t03zBE/7TTYf3+VuIjkhNpZ6B9++HOJz5kTth10EAwcGM7E9bxN\nEclBtaPQ3cOCV0VF4bb7998P21u2hMGDw7ope+wRN6OISA3lb6G7h6VnfzoT/+STMHRy9NFw772h\nxJs2jZ1SRCRl8qvQy8rgzTd/XvxqwYLwGLbWreGqq6BLF9h559gpRUTSIvcLvbQUpk8PZ+HPPAOL\nFoUbe9q2hQEDoFMnaNQodkoRkbTLzUJfty48GLmoKDwoedmycIt9+/bhoubJJ8P228dOKSKSUblT\n6GvWhLs0x4yBCRNgxQrYbrtQ3l27wkknhcWwRERqqdwo9D/9Cf7yl7AYVsOGYSy8a9cwrFKvXux0\nIiJZITcKvWlTOPvsUOLHHx8eECEiIr+QG4V+wQXhS0REKqTVpURE8oQKXUQkT9So0M3sJDP7yMw+\nMbP+qQolIiJVV+1CN7M6wP1Ae2Bf4Ewz2zdVwUREpGpqcoZ+GPCJu3/q7uuAUUDn1MQSEZGqqkmh\n7wp8udH3XyW2iYhIBGm/KGpmvcys2MyKS0pK0r07EZFaqyaF/jXQbKPvmya2/YK7D3P3QncvLCgo\nqMHuRERkc8zdq/eDZlsCHwNtCEX+NnCWu7+3mZ8pAb6o1g6hMbCsmj+bTspVNcpVNcpVNdmaC2qW\n7VfuXukZcbXvFHX3UjPrA7wI1AFGbK7MEz9T7VN0Myt298Lq/ny6KFfVKFfVKFfVZGsuyEy2Gt36\n7+6TgEkpyiIiIjWgO0VFRPJELhX6sNgBKqBcVaNcVaNcVZOtuSAD2ap9UVRERLJLLp2hi4jIZmRV\noZvZCDNbambzKnjfzOy+xGJg75jZwVmS6zgzW2FmcxNff8xQrmZmNs3M3jez98ysbzmfyfgxSzJX\nxo+ZmdUzs7fM7J+JXAPK+czWZjY6cbxmmlnzLMl1vpmVbHS8fpfuXBvtu46ZzTGzieW8l/HjlWSu\nKMfLzD43s3cT+ywu5/30/j66e9Z8AccABwPzKni/A/A8YEBLYGaW5DoOmBjheDUBDk683o5wX8C+\nsY9ZkrkyfswSx6BB4nVdYCbQcpPP9AYeTLzuDozOklznA0My/Xcsse8rgSfK+98rxvFKMleU4wV8\nDjTezPtp/X3MqjN0d58OfLuZj3QG/u7BDGBHM2uSBbmicPdF7j478fp74AP+cz2djB+zJHNlXOIY\nrEp8WzfxtelFpM7AyMTrMUAbM7MsyBWFmTUFOgLDK/hIxo9XkrmyVVp/H7Oq0JOQzQuCHZH4J/Pz\nZvabTO888U/dgwhndxuLesw2kwsiHLPEP9PnAkuBye5e4fFy91JgBbBTFuQC6Jr4Z/oYM2tWzvvp\ncA9wDVBWwftRjlcSuSDO8XLgJTObZWa9ynk/rb+PuVbo2Wo24dbcA4G/AuMyuXMzawAUAVe4+8pM\n7ntzKskV5Zi5+wZ3b0FYe+gwM9svE/utTBK5JgDN3f0AYDI/nxWnjZmdDCx191np3ldVJJkr48cr\n4Sh3P5jwnIhLzeyYDO0XyL1CT2pBsExz95U//ZPZw92zdc2scSb2bWZ1CaX5uLuPLecjUY5ZZbli\nHrPEPpcD04CTNnnr38fLwnpFOwDfxM7l7t+4+9rEt8OBQzIQpxXQycw+JzzvoLWZPbbJZ2Icr0pz\nRTpeuPvXiT+XAs8QnhuxsbT+PuZaoT8LnJe4UtwSWOHui2KHMrNdfho3NLPDCMc17SWQ2OcjwAfu\nflcFH8v4MUsmV4xjZmYFZrZj4nV9oC3w4SYfexbokXh9GvCyJ65mxcy1yThrJ8J1ibRy92vdvam7\nNydc8HzZ3c/Z5GMZP17J5IpxvMxsWzPb7qfXQDtg05lxaf19rNFaLqlmZk8SZj80NrOvgJsIF4hw\n9wcJ68Z0AD4BVgMXZEmu04BLzKwUWAN0T/df6oRWwLnAu4nxV4DrgN02yhbjmCWTK8YxawKMtPD4\nxC2Ap9x9opndAhS7+7OE/yN61Mw+IVwI757mTMnmutzMOgGliVznZyBXubLgeCWTK8bx2hl4JnGe\nsiXwhLu/YGa/h8z8PupOURGRPJFrQy4iIlIBFbqISJ5QoYuI5AkVuohInlChi4jkCRW6iEieUKGL\niOQJFbqISJ74f1fhm0l3vA93AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105145080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, \"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will give you the same line as before, but now it'll be red. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Styles\n",
    "\n",
    "You can also customize the style of the your lines and markers. With line graphs, you can change the line to be dotted, dashed, etc, for example the following should give you a dashed line now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1054a7390>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7F7h7QQvNMUoumjQJCgpg7dpwq7hbb42dSKRCNS30qcCQxOMhwAupiSOSZRYsgAsvhI4d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1053eab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y, \"r--\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find other linestyles you can use can be found on the [Matplotlib webpage](http://\n",
    "matplotlib.sourceforge.net/api/pyplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With Scatter Plots, you can customize the dots to be squares, pentagons, etc. This will get you the a scatter plot with star markers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10559f400>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xiq8Dvl9V8+MOlWTLc/cNk1xLb16HXgLdmLcDj1XVh5c4beRz1k+uccxZkpkkL+7WNwGv\nBb550Wl3A7d1628Cvlrdq1njzHXRfdbX03tdYqiq6o+r6mVVtZXeC55frarfuui0kc9XP7nGMV9J\nXpjkRc+tA68DLn5n3FB/Hifqj0Qn+TS9dz9sTvIk8H56LxBRVR+l970xNwOPA/8N/M6E5HoT8HtJ\nzgI/AN487P+oO9cDbwEe6e6/ArwPuPqCbOOYs35yjWPOZoE70/vziT8GfLaqvphkHzBXVXfT+x/R\nXyd5nN4L4W8ecqZ+c/1BktcDZ7tcbx1BrkVNwHz1k2sc83UlcFd3nbIB+FRVfTnJXhjNz6OfFJWk\nRqy3Wy6SpCVY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNeL/AKwXNhuRxRm+AAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105484358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y, \"r*\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labels\n",
    "\n",
    "We want to always label the axes of plots to tell users what they're looking at. You can do this in matplotlib, very easily:\n",
    "\n",
    "If we want to attach a label on the x-axis, we can do that with the `xlabel()` function: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1054b1ac8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1055c3160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"X Axis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a quick modification, we can also do that for the y-axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1056c1d68>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vAa4DdlXVT0aUbdRWmoszgdcAX03yPXrHYGdO0RPaw/xcnABmquqnVXU/8F16xXGqGWYu\nrgQOAFTVN4Dn0rth4Hoz1P8ni01qUXj7jwUrzkWS1wGfpFcSp+pxaFhhLqrq8araXFVnVdVZ9M7X\n7KqqNd8MbYIN8zvyRXp7EyTZTO9Q1PFRhhyRYebiAeAigCSvplcU8yNNORlmgHf03/10PvB4VT2y\n0osm8tBTdXf7j5POkHPxEeD5wG398/kPVNWusYXuyJBzsS4MOReHgLcmOQb8DLimqk65ve4h5+Jq\n4FNJ/pTeie13nYp/WCb5HL0/Djb3z8dcD5wGUFU30Ts/cwkwBzwBXDHUdk/BuZIkPYsm9dCTJGlC\nWBSSpCaLQpLUZFFIkposCklSk0UhSWqyKCRJTf8Ho04d7QyPoxUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1056622e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylabel(\"Y Axis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What good is a visualization without a title to let us know what the visualization is showing? Luckily, matplotlib has a built in function for that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10573be48>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Sm5NsSbJqzqqbW3364lLg/CS7gU3AG+amtMPOY80TYI6HVlA/Sc4HxoEXzXct8yHJEcD7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\ntCwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1057ae240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Title Here\")\n",
    "plt.xlabel(\"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we can even customize the range for the x and y axes: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10588c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim(0, 10)\n",
    "plt.ylim(0, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn\n",
    "\n",
    "[seaborn](http://seaborn.pydata.org/introduction.html#introduction) is a library for making statistical graphics in Python. It's built on top of matplotlib and is tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from urllib.request import urlretrieve\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('task_data.csv', <http.client.HTTPMessage at 0x107347278>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://gist.githubusercontent.com/jhamrick/cfa18fcd3032ba435ec78a194b1447be/raw/4a4052c56161df8e454a61ab5286a769799c64b8/task_data.csv\"\n",
    "urlretrieve(url, \"task_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>robot</th>\n",
       "      <th>inference</th>\n",
       "      <th>human_tasks</th>\n",
       "      <th>robot_tasks</th>\n",
       "      <th>both_tasks</th>\n",
       "      <th>total_tasks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00d648c5</td>\n",
       "      <td>fixed</td>\n",
       "      <td>oracle</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00d648c5</td>\n",
       "      <td>predictive</td>\n",
       "      <td>oracle</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00d648c5</td>\n",
       "      <td>reactive</td>\n",
       "      <td>oracle</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01276036</td>\n",
       "      <td>fixed</td>\n",
       "      <td>oracle</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01276036</td>\n",
       "      <td>predictive</td>\n",
       "      <td>oracle</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pid       robot inference  human_tasks  robot_tasks  both_tasks  \\\n",
       "0  00d648c5       fixed    oracle           15           15           9   \n",
       "1  00d648c5  predictive    oracle           23           15           1   \n",
       "2  00d648c5    reactive    oracle           18           17           4   \n",
       "3  01276036       fixed    oracle           25           11           3   \n",
       "4  01276036  predictive    oracle           25           14           0   \n",
       "\n",
       "   total_tasks  \n",
       "0           39  \n",
       "1           39  \n",
       "2           39  \n",
       "3           39  \n",
       "4           39  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_data = pd.read_csv(\"task_data.csv\")\n",
    "task_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nauJ/Acz2XLpusNoKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1073470b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "robot       inference\n",
      "fixed       bayesian     11.474227\n",
      "            oracle       11.696970\n",
      "predictive  bayesian     16.814433\n",
      "            oracle       15.121212\n",
      "reactive    bayesian     12.793814\n",
      "Name: robot_tasks, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Container object of 6 artists>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tasks = task_data.groupby(['robot', 'inference'])['robot_tasks'].mean()\n",
    "print(tasks.head())\n",
    "ax.bar(np.arange(len(tasks)), tasks, align='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the 50% mark, which would indicate an equal number of tasks being completed by the robot and the human. There are 39 tasks total, so 50% is 19.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x107ca54a8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.hlines(19.5, -0.5, 5.5, linestyle='--', linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set a reasonable y-axis limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 40)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.set_ylim(0, 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply labels to the bars so you know which is which"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.set_xticks(np.arange(len(tasks)))\n",
    "ax.set_xticklabels([\"\\n\".join(x) for x in tasks.index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's import seaborn and see what happens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, if we re-run the code, we'll see the different default graph for seaborn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x109c07828>,\n",
       " <matplotlib.text.Text at 0x109bfb9b0>,\n",
       " <matplotlib.text.Text at 0x109c63ac8>,\n",
       " <matplotlib.text.Text at 0x109c695c0>,\n",
       " <matplotlib.text.Text at 0x109c6f0b8>,\n",
       " <matplotlib.text.Text at 0x109c6fb70>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tCvvwxj5qsAtv7MNbS9hHvcM7YMAA5ebmatiwYdq1a5euv/56n/cpLj7VoOFQo7CQVxb+\nF/vwxj5qsAtv7MObzX3UFvl6/zpRYmKi0tLSNHXqVLndbkVGRl7wcAAAtBbndcbbvXt3bd26VZLU\np08fbdy4sUmHAgDAX3EBDQAALCK8AABYRHgBALCI8AIAYBHhBQDAIsILAIBFhBcAAIsILwAAFhFe\nAAAsIrwAAFhEeAEAsIjwAgBgEeEFAMAiwgsAgEWEFwAAiwgvAAAWEV4AACwivAAAWER4AQCwiPAC\nAGAR4QUAwCLCCwCARYQXAACLCC8AABYRXgAALCK8AABYRHgBALCI8AIAYBHhBQDAIsILAIBFzobe\n8fbbb1dwcLAkqXv37lq2bFmjDQUAgL9qUHgrKipkjFFmZmZjzwMAgF9r0EvNn376qX766SfFx8dr\n+vTp2rdvX2PPBQCAX2rQGe+ll16qmTNnKioqSl9++aVmzZqlv/3tb3I6z/3hOnZsL6cz8IIGbe3C\nw0Oae4QWhX14Yx812IU39uGtJeyjQeHt06ePevXqJYfDoT59+qhDhw4qLCxUt27dzvn+xcWnLmhI\nSIWFpc09QovCPryxjxrswhv78GZzH7VFvkEvNb/66qt68sknJUnff/+9ysrKFB4e3vDpAABoJRp0\nxjt58mQtWLBA0dHRcjgcWrp0aa0vMwMAgBoNqmXbtm319NNPN/YsAAD4PS6gAQCARYQXAACLCC8A\nABYRXgAALCK8AABYRHgBALCI8AIAYBHhBQDAIsILAIBFhBcAAIsILwAAFhFeAAAsIrwAAFhEeAEA\nsIjwAgBgEeEFAMAiwgsAgEWEFwAAiwgvAAAWEV4AACwivAAAWER4AQCwiPACAGAR4QUAwCLCCwCA\nRYQXAACLCC8AABYRXgAALCK8AABY5GzInTwejxYvXqzPPvtMbdu21eOPP65evXo19mwAAPidBp3x\n7ty5U2fOnNGWLVv05z//WU8++WRjzwUAgF9qUHg//vhjDR8+XJI0aNAgHTx4sFGHAgDAXzmMMaa+\nd1q4cKHGjRunm2++WZI0cuRI7dy5U05ng165BgCg1WjQGW9wcLDKy8ur/+3xeIguAADnoUHhHTx4\nsHbt2iVJ2rdvn37729826lAAAPirBr3U/PNPNR8+fFjGGC1dulT9+vVrivkAAPArDQovAABoGC6g\nAQCARYQXAACLCC8AABYR3nOorKyUy+XSH/7wB7322msN/jgvv/yy0tLSGnGy5sE+zs/DDz+s3Nxc\n7dq1S1u2bKn1/bZs2SK3261Dhw5pzZo1Fie0i3349s9//lOffvqpJGnOnDnNPE3zay374Jdvz6Gg\noEDl5eV6//33m3uUFoF91M+IESPqfPu6det02223qX///urfv7+lqZoP+6hddna2Jk6cqKuvvrrV\nfdFxLq1lH4T3HFJSUvTll19q0aJF6t+/v7p27aoXX3xRGzdu1Jo1a3T69GnNmzdPTz/9tD766CN5\nPB7NmDFDEyZM0EcffaSlS5cqNDRUgYGBGjRoUHM/nQvWGvaxbds27dy5U+Xl5SouLtbs2bOVlpam\n3r17q02bNnrssce0cOFCFRcXS5KSk5N11VVXKSsrS6+88orCw8P1ww8/VH+sL774QnPnzlV6erp2\n7typqqoqRUdHKzAwUIWFhXr44Yd11113afPmzbrlllu0c+dOLVu2TJJ0++236y9/+Ys+/PBDvfTS\nSwoICNB1112nuXPnso9m2scvd5OdnS2PxyOXy6WMjAyvmb777jstXrxYFRUVKiwsVEJCgsaMGaN3\n3nlHa9askTFGAwcO1NSpU/WPf/xDeXl5uuKKKxQVFaU33nhD06ZN05tvvimHw6HHHntMN9xwg3r2\n7KnHH39cktShQwctXbpUISEhzfL8f4l9NIDBr/z3v/81UVFRZvXq1WbTpk3GGGNSU1PN3LlzTWxs\nrHG73ebdd981CQkJxhhjTp8+bSZNmmROnjxpbrnlFvPFF18YY4xZtGiRWb16dbM9j8bSGvaRnZ1t\nZsyYYaqqqkxhYaEZOXKkGT58uMnLyzPGGPPUU0+ZrKwsY4wx+fn55s477zSFhYVm3LhxpqKiwpw5\nc8bccsstZu/evSY7O9usWLHC5OXlmalTp5rKykpTUVFhli1bZjwejxk1apQ5ffq02bt3r0lISDCV\nlZUmMjLSlJeXm/3795sHHnjAFBcXmwkTJphTp04ZY4yZO3euef/999lHM+3jl7u57777ap3pgw8+\nMHv37jXGGPPxxx+bGTNmGLfbbUaNGmWKioqMMcasX7/eHD9+3CQmJpr33nvPGGPMjTfeaIwx5qGH\nHjIffvihqaioMBMnTjRut9tERUWZzz//3BhjzNatW80zzzxj+2nXin3UH2e852nWrFkaNWqUnnvu\nOTmdTh0+fFh5eXlyuVySzn4f9Pjx4yoqKlKfPn0knb3C17Fjx5pz7Cbjj/v4/e9/r4CAAHXu3Fmh\noaE6evRo9eyHDx/W3r179dZbb0mSTp48qWPHjumKK65Q27ZtJUkRERFeHy8/P18REREKDAxUYGCg\n5s+ff87HDQwMVGRkpN5++23t27dPUVFROnbsmH788Ufdc889kqTy8nIdO3ZMN910U1M9/V9hH7Xr\n06dPrTMNGTJEa9eu1auvviqHw6HKykoVFxcrNDRUnTp1knT286c2U6ZM0WuvvabCwkKNHj1aTqdT\nR48e1ZIlSyRJbrdbvXv3bvLnWB/so3744arzlJKSooULFyotLU0nT55U3759NWzYMGVmZiojI0MT\nJkxQjx491KVLFx09elSS9J///KeZp246/riPvLw8SVJRUZHKysrUqVMnBQSc/RTp27evZsyYoczM\nTD333HOaNGmSevfurSNHjuj06dOqqqrSoUOHvD5e37599cknn8jj8cjtdisuLk5nzpyRw+GQx+Px\net/Jkydr+/btOnDggG666SZ1795d3bp104YNG5SZmanY2FjrL9Ozj9oFBATUOtOqVav0pz/9SStW\nrNCwYcNkjFGnTp1UUlKiEydOSJIef/xxHThwQA6HQ+YX1zC64YYbdOjQIWVnZysqKkrS2bAtX75c\nmZmZeuSRRzRy5EjbT7lO7KN+OOM9DxkZGerUqZOmTZumdu3aKTk5WatXr9aHH36omJgYnTp1SmPG\njFFwcLAee+wxzZs3T8HBwQoKCtJvfvOb5h6/0fnrPoqKinTXXXeptLRUKSkpWrx4cfXb7rvvPi1c\nuFBbt25VWVmZ5syZo7CwMM2aNUt33nmnwsLC1K5dO6+P179/fw0fPlzR0dHyeDyKjo5W27ZtNWTI\nEN1zzz2aPXt29fv26NFDkjR69GgFBAQoLCxMM2bMkMvlUlVVlS6//HJNmDDByh5+xj7qVttM48eP\n11NPPaX169era9euKi4uVkBAgFJSUnTvvfcqICBAAwYM0LXXXqtPPvlEK1euVPfu3as/rsPhUGRk\npHbv3q2ePXtKkhYvXqzExERVVlbK4XDoiSeeaK6nXSv2cf64ZCQg7x8AAvsAmhIvNQMAYBFnvAAA\nWNTqzni3bdumlStXNvnjrF+/XgcOHGjyx7kYuFyu6h+wask4Nuzj2PDGsVHjYjk2GoIfrmoiP/9I\nPfBLHBuoDcdG69Aqw7tv3z7dddddKisr0wMPPKDTp08rKyur+ifk1qxZo5deekldunTRtGnTdPLk\nScXFxWnbtm3nvDpTVlaWXn/9dQUEBOjaa69VcnKy5s+fr4kTJ2rw4MFauHChSktLVVBQoJiYGMXE\nxMjlcunqq6/W559/rrKyMq1atUqXX355c6+mmtvt1oIFC/T111+rqqpKcXFxevnllxUWFqaTJ08q\nLS1NycnJv3pe+/fv19KlS+XxeNSlSxevs4TS0tJzXu2oJeHY8I1jg2OjNq312Ki35rt2R/PIzs42\nd999t/F4PKaoqMiMGjXKrF27tvpqK48++qj561//ao4dO2YmT55sjDFm48aNZsOGDbVenemOO+4w\n+/fvN8YYk5WVZdxud/UVWA4ePGj+/ve/G2OM+e6778zYsWONMcbExsaa7du3G2OMeeaZZ8y6deus\n7sGXzMxM88QTTxhjjCktLTVjx441f/zjH83bb79tjDG1Pq9JkyaZI0eOGGPOXlHm4MGDJjY21hw5\ncuScVztqSTg2zg/HBsdGbVrjsdEQrfKM97rrrpPD4VCnTp0UEhIip9OpxMREBQUF6YsvvtCgQYPU\no0cPBQUF6ciRI3rjjTeUnp6u7Ozsc16dadmyZdqwYYOeeuopDRo0yOsXwDt37qyMjAy9/fbbCg4O\nVmVlZfXbBgwYIEnq2rWrioqK7C7Bh6NHj+rGG2+UJAUHB6tfv3764IMPqq9cVNvzKioqUr9+/SSp\n+pfdf3auqx21NBwbvnFscGzUprUeG/XVKsP78xWUCgsLVVpaqoyMDL377ruSpLi4uOpPgClTpig9\nPV1dunRRWFhY9dWZUlNT5fF4lJ6erh49eui5557TkiVLdMkll2jmzJn697//Xf1YGzZs0KBBgxQT\nE6O9e/fqvffes/58G6Jfv3766KOPNHbsWJWVlenw4cPq3r27HA6HpNqf12WXXaYvv/xSvXv31vr1\n66s/4aSzVy6aNGmSbr31Vv3www965ZVXmuW51YVjwzeODY6N2rTWY6O+Wt1PNUvS6dOnNX36dN1/\n//164oknNHjwYE2dOlXTpk3TpZdeqoKCAknSmDFjtHv3bk2ePFnS2avotG/fXjExMbrjjjsknf2q\n7qqrrlJMTIymT5+usLAw/e53v6t+rFGjRmnTpk2KjY1VRkaGAgMDdebMGftPup6mTJmiEydOKDo6\nWtOnT9ecOXOqr6sq1f68lixZoqSkJMXGxurQoUO6+eabq+9z33336a233pLL5dLdd9+tK6+8sjme\nWp04Nnzj2ODYqE1rPTbqi9/jrcNPP/2k2NhYvfLKK9XXqAUkjg3UjmMDvnBU1OJf//qXpkyZolmz\nZvHJAy8cG6gNxwbOB2e8AABYxJdkAABYRHgBALCI8AIAYBHhBQDAIsILAIBFhBcAAIv+H9oRNNYu\nZftEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b5f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(np.arange(len(tasks)), tasks, align='center')\n",
    "ax.hlines(19.5, -0.5, 5.5, linestyle='--', linewidth=1)\n",
    "\n",
    "ax.set_ylim(0, 40)\n",
    "ax.set_xticks(np.arange(len(tasks)))\n",
    "ax.set_xticklabels([\"\\n\".join(x) for x in tasks.index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The barplot function gives us a legend telling us which color corresponds to which inference type. But, for this plot, I'd actually like to put the inference types under the bars as ticklabels, and then label each group of bars with the robot type. I can accomplish this by splitting the plot into three subplots, which is quite easy to do using Seaborn's FacetGrid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lpT/84Q+aOXOmzp8/r8jISOXn5ysjI0NhYWHq0aOHDhw4oDVr1sgYo7Zt22rk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\nj7BeigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109c804a8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FRcnb++bD4xkZJe+SBaqC0oxE0Buo6ugLoLiS+sJjga4s0JyoDvjiAoqjL4DiKmSXKwAA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\nj7BeigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109c804a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(\n",
    "\ttask_data,\n",
    "\tcol=\"robot\",\n",
    "\tcol_order=[\"fixed\", \"reactive\", \"predictive\"],\n",
    "\tsharex=False)\n",
    "\n",
    "    # Create the bar plot on each subplot\n",
    "g.map(\n",
    "\tsns.barplot,\n",
    "\t\"robot\", \"robot_tasks\", \"inference\",\n",
    "\thue_order=[\"oracle\", \"bayesian\"])\n",
    "\n",
    "    # Now I need to draw the 50% lines on each subplot\n",
    "    # separately\n",
    "axes = np.array(g.axes.flat)\n",
    "for ax in axes:\n",
    "\tax.hlines(19.5, -0.5, 0.5, linestyle='--', linewidth=1)\n",
    "\tax.set_ylim(0, 40)\n",
    "\n",
    "plt.gcf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ggplot\n",
    "\n",
    "Ggplot is yet another module we can use for data visualizations in Python. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ggplot/utils.py:81: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n",
      "You can access Timestamp as pandas.Timestamp\n",
      "  pd.tslib.Timestamp,\n",
      "/usr/local/lib/python3.6/site-packages/ggplot/stats/smoothers.py:4: FutureWarning: The pandas.lib module is deprecated and will be removed in a future version. These are private functions and can be accessed from pandas._libs.lib instead\n",
      "  from pandas.lib import Timestamp\n",
      "/usr/local/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "data": {
      "image/png": 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Raq4CoFFfjKn0nLpL8fnxfvM+1LR0Qt+8G7uttdc5bMVHo74Qr12Nz6oc3Bsj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J3AA7nuLTwvQXHANOzaiViaQUNNgiqvgO0oehND6Bp8MattH5HLkn7wIWpjOlVHTqJN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\nuzfM96WrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ace0da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (274176569)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ggplot import *\n",
    "ggplot(diamonds, aes(x='price', color='clarity')) + \\\n",
    "    geom_density() + \\\n",
    "    scale_color_brewer(type='div', palette=7) + \\\n",
    "    facet_wrap('cut')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bokeh\n",
    "\n",
    "[bokeh](http://bokeh.pydata.org/en/latest/) is an interactive visualization library for modern web browsers presentation. When we output a visualization, you'll notice that an HTML file is opened in your local browser. \n",
    "\n",
    "To begin, we'll import `bokeh` and its needed classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bokeh.charts import Histogram\n",
    "from bokeh.plotting import show\n",
    "from bokeh.io import output_notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have imported the modules, we need data to display. We'll be using tip data for waiters and waitresses!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purpose of an example, we'll display a histogram using bokeh. Since histograms show frequency, we'll select the tips column and visualize that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hist = Histogram(tips['tip'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of the `plt.show()` function we've been using up until this point, `bokeh` actually has its own built in show function, so we can simply call that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "show(hist)"
   ]
  }
 ],
 "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
}