nbformat3.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:3b45e82a4182cb7ff1f1ccc3f5ef9e40b3e59c490fed12b477911d25743802aa"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import seaborn\n",
      "import matplotlib\n",
      "print seaborn.__version__\n",
      "print matplotlib.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.3\n",
        "1.3.1\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "tips = sns.load_dataset(\"tips\")\n",
      "\n",
      "# blank panels\n",
      "g = sns.FacetGrid(tips, col=\"day\", col_wrap=3)\n",
      "g.map(plt.hist, \"tip\")\n",
      "g.set(xlim=(0, 12), ylim=(0, 30))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<seaborn.axisgrid.FacetGrid at 0x111c85850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAGqCAYAAACbEvXuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABZDAAQAALCY/w8zVcLsg+N+dQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111d49190>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = sns.FacetGrid(tips, col=\"day\", col_wrap=1, aspect=1)\n",
      "g.map(plt.hist, \"tip\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANAAAANaCAYAAAD4Z8JXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9UlPWeB/D3yIyoCAr+OtvWdKQ1rI4/Qvpxvf5gMVZd\ns0sBAnKxgjUbV/IgIqQbcjiUqEvkBnjYk1fX0asDmduxTD0dUO+lSFwPdqgVb2jazVJ+KDEIPSPz\n7B+tcyGEYD74zDC9X3/FfJ9nPt8v9p7nAWa+H52qqiqIyClDXD0BosGMASISYICIBBggIgEGiEiA\nASIS0Lt6AvST7OxsTJkyBc8+++xdrzV58mRMnjy5W/2pU6d2eezf/u3fEBcXh0ceeeSuz2mwYoDc\nhE6n07Tef//3f//iMTk5ORrMZHBjgFxo8+bNKCsrw9ixY2EwGDBlyhQAQH5+PiorK3Hjxg34+/uj\noKAA5eXlqKysRF5eHgCgoKAA3t7eWL58ueP58vPzceLEiS41nnnmGSQmJvZpPm+//Taqq6vx/fff\nIz4+Hh999BGSk5Px+OOPD9CKPQ8D5CJHjx5FTU0NDh8+DKvV6rh1u3z5Mi5evAiLxQIASE9Px6FD\nhxATE4O33noLbW1tGDZsGA4dOoQ9e/Z0ec6UlBSkpKT0qX5ERITjv5988klkZGQAAGw2Gz788EMA\nwEcffaT5lXGwYYBc5NSpU5g/fz68vLwwatQozJs3D6qqwmg0Ij09HRaLBRcvXkR1dTWMRiNGjBiB\nOXPm4OjRo7j33nthNBoxbty4Ls95pyvQ4sWLkZSU1K3+nW7hdDodpk2bNrAL9XAMkIvodDrY7XbH\n13r9T/8UNTU1SE1NRWJiIhYsWAAvLy/cfrtiZGQktm/fjvvuuw/PPfdct+fszxWoJ97e3qLzf234\na2wXmTlzJg4fPgxFUWC1WlFeXg4AOH36NJ544gnExMTggQceQEVFBTo6OgAAISEhuHr1Kk6dOoWn\nnnrKldOn/8crkIuEhYWhpqYGixcvhr+/PwIDA6HT6bBw4UIkJycjIiIC/v7+mDNnDr799lvHeeHh\n4WhubobBYHC6Nn+uGTg6fpxh8FAUBYmJidiwYQMeeughV0+H4OQtnN1uR2ZmJmJjY5GQkIDLly93\nGS8rK0NUVBRiY2NRWlrqeLy4uBixsbGIjIzEwYMHZTP/lbl27RpmzZqF6dOnMzzuRHXC0aNH1YyM\nDFVVVbW6ulo1mUyOMUVR1PDwcPWHH35QFUVRIyMj1YaGBrWyslJdsWKFqqqq2traqm7bts2Z0kRu\nxamfgc6cOYPZs2cDAKZNm4aamhrHWF1dHYxGI3x9fQEAM2bMQFVVFb788ksEBQVh5cqVsFqtWLdu\n3QDEn8i1nAqQ1WrFyJEjHV97eXnBbrdjyJAhsFqtjvAAgI+PD1paWnD9+nVcuXIFxcXF+Oabb2Ay\nmXDkyBH5CohcyKkAjRw5Eq2trY6vb4cHAHx9fbuMtba2ws/PD6NHj0ZgYCD0ej0mTpwIb29vNDU1\nISAgoMc6qqryN0bk1pwKUHBwMMrLy7Fw4UJUV1cjKCjIMRYYGIhLly6hubkZw4cPR1VVFZKSkuDt\n7Y3du3fjxRdfxNWrV9HW1gZ/f/9e6+h0OtTXtzgzxT4bN873rtfQqo4nrUWrOuPG+f7yQb1wKkDh\n4eGoqKhAbGwsAGDTpk344IMPcPPmTSxZsgQZGRlISkqC3W5HVFQUxo8fj/Hjx6OqqgpRUVGw2+3Y\nuHEjry406Ln934H4qu1eNTytjvQKxLfyEAkwQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkw\nQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCmnZnAIDGxkbM\nnTsXFy9edH7WRG7CqQB9/PHHsNls2L9/P9auXYvc3FzHmM1mQ25uLnbu3Amz2QyLxYLGxkbHWGZm\nJoYPHz4wsydyMacC1NfuDAaDwdGdAQC2bNmCuLi4bs1xiQYrzbozvPfeewgICMCsWbNQXFyMvm6I\nKt050l1qaFXHk9aiZR1nadadwWw2Q6fT4ZNPPsG5c+eQkZGBoqIijB07ttda3A7XvWp4Wh2XbC7v\nTHeG+fPnO45JSEhAdnb2L4aHyN1p1p2ByBOxO4OH3Y54ylq0qsPuDEQuxAARCTBARAIMEJEAA0Qk\nwAARCTBARAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIMEJEA\nA0Qk4NS2Vna7HVlZWTh//jwMBgNef/11GI1Gx3hZWRmKioqg1+sRGRmJ6Oho2Gw2rF+/HleuXIGi\nKDCZTAgLCxuwhRC5glMB6ry5/NmzZ5Gbm4uioiIAf9tc/sCBAxg2bBji4uIQFhaGEydOICAgAFu3\nbkVzczMiIiIYIBr0nApQXzeXB+DYXH7BggWO3Untdju8vLykcydyOc02lx8xYoTj3NWrVyMlJUU4\ndSLX02Rz+VGjRgEAvvvuO6xatQrx8fFYtGhRn2qxo4H71fDEOs7SbHP5hoYGJCYmYuPGjXjyySf7\nXIvb4bpXDU+r45LuDM5sLp+Tk4OWlhYUFhaisLAQAPDOO+/A29tbtAAiV+Lm8h72auopa9GqDjeX\nJ3IhBohIgAEiEmCAiAQYICIBBohIgAEiEmCAiAQYICIBBohIgAEiEmCAiAQYICIBBohIgAEiEmCA\niAQYICIBBohIgAEiEmCAiAQYICIBBohIQLPuDL90DtFg5NQVqHN3hrVr1yI3N9cxdrs7w86dO2E2\nm2GxWNDY2NjrOUSDlWbdGaqrq3s8h2iwcuoK1FN3httjd+rO0Ns5RIOVJt0Z/Pz8ej2nN+xo4H41\nPLGOszTrzqDT6Xo8pzfcT9q9anhanUHTneFO5xANduzO4GGvpp6yFq3qsDsDkQsxQEQCDBCRAANE\nJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCR\nAANEJMAAEQkwQEQC/d4Xrr29HWlpaWhqaoKPjw9yc3MREBDQ5ZiSkhJYLBbo9XqYTCaEhoaipaUF\naWlpaG1thc1mQ0ZGBqZPnz5gCyFyhX5fgfbt24egoCDs3bsXERER2L59e5fx+vp6mM1m7N+/Hzt2\n7EBeXh4URcGuXbswc+ZMmM1mbNq0CdnZ2QO2CCJX6XeAzpw5gzlz5gAAZs+ejU8//bTL+Oeff47g\n4GAYDAaMHDkS999/P2pra/HCCy8gJiYGAHDr1i14e3sPwPSJXKvXW7jS0lLs3r27y2NjxoyBj48P\ngL91XuistbW1W3eGzh0b6uvrsW7dOmzYsGFAFkDkSr0GKDo6GtHR0V0eS05OdnRZuN15obOfd2Ho\nfExtbS1SU1ORnp6OkJCQPk2QHQ3cr4Yn1nFWv3+JEBwcjJMnT2Lq1Kk4efJktyBMnToV+fn5UBQF\nP/74I+rq6jBp0iR89dVXWL16NbZt29bnzgwA98Z2txqeVkfz7gxxcXFIT0/H0qVLMXToUOTl5QEA\ndu3aBaPRiLCwMCxbtgxLly6F3W7HmjVrMHToULz55puw2WzIyckBAPj5+aGwsFA0eSJXY3cGD3s1\n9ZS1aFWH3RmIXIgBIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIAB\nIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIABIhLod4Da29uRnJyM+Ph4vPTSS2hqaup2TElJ\nCSIjIxETE4Pjx493Gaurq0NISAgURXF60kTuQrPuDABgtVqxefNmbixPHkOz7gyqqiIzMxNr1qxh\ngMhjaNadoaCgAHPnzsXkyZMHau5ELqdJdwZfX18cOnQIEyZMwLvvvouGhgYkJSXBbDb/4gTZ0cD9\nanhiHWdp0p3hwQcfxLFjxxzHhIWFYceOHX2qx/2k3auGp9UZNN0ZOtPpdKJJE7kLdmfwsFdTT1mL\nVnXYnYHIhRggIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEG\niEiAASISYICIBBggIgEGiEiAASISYICIBBggIoF+7wvX3t6OtLQ0NDU1wcfHB7m5uQgICOhyTElJ\nCSwWC/R6PUwmE0JDQ9HR0YFNmzbhiy++gM1mwyuvvOLYY5tosNKsO8P777+Pjo4O7Nu3D4WFhbhw\n4cKALYLIVTTrzlBRUYEJEyZgxYoVeO211zBv3ryBWQGRC2nWneH69eu4fPkyiouLUVVVhVdffRV7\n9uwZqHUQuYRm3RlGjx6N0NBQAMBjjz2Gr7/+uk8TZEcD96vhiXWc1e9buNvdGQD02J3h9OnTUBQF\nLS0tju4MM2bMwIkTJwAA586dwz333DMA0ydyrX5vLt/e3o709HTU19c7ujOMGTOmS3eG0tJSWCwW\n2O12mEwmhIeHQ1EUZGVloa6uDgCQlZWFhx566K4sikgrbt+dgcid8Q+pRAIMEJEAA0QkwAARCTBA\nRAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAC5WHZ2Ng4ePKhJrc8++wyxsbH43e9+h6effhpbt26F\n3W7v9ZyysjLs2rVLk/kNRgyQi+l0Ok3qKIqC1NRU5OXl4f3338fBgwdRV1eHvXv39nreF198AavV\nqskcB6N+bypCcps3b0ZZWRnGjh0Lg8GAKVOmAADy8/NRWVmJGzduwN/fHwUFBSgvL0dlZSXy8vIA\nAAUFBfD29sby5csdz5efn+/4rNVtzzzzDBITEx1ft7W1obW1FTdv3gQAGAwGbNiwAW1tbQCAU6dO\n4a233kJ7ezuam5uRlpaGSZMmYf/+/dDpdPj7v/97PPvss3f1+zIoqaSpI0eOqL///e/VW7duqTdu\n3FD/8R//UT148KB66dIlNTk52XHcunXr1D/84Q9qa2urOnPmTPXmzZuq3W5X/+mf/km9du2aU7W3\nb9+uPvLII+rixYvVnJwc9fTp046x5ORk9cKFC6qqquonn3yiPv3006qqqurbb7+tvv3224IVezZe\ngTR26tQpzJ8/H15eXhg1ahTmzZsHVVVhNBqRnp4Oi8WCixcvorq6GkajESNGjMCcOXNw9OhR3Hvv\nvTAajRg3blyX57zTFWjx4sVISkrq8tjLL7+M2NhYVFRUoKKiAsuXL8fq1avx/PPP49///d9RVlaG\njz76CGfPnnVcmVRV1ew2czBigDSm0+m6/OCu1//0T1BTU4PU1FQkJiZiwYIF8PLygvr/n3WMjIzE\n9u3bcd999+G5557r9pwpKSlISUnpte7Zs2dRU1OD+Ph4LFq0CIsWLcLTTz+NN954A88//zzi4uLw\nm9/8Bo8//jh+85vfIDU1dQBX7bn4SwSNzZw5E4cPH4aiKLBarSgvLwcAnD59Gk888QRiYmLwwAMP\noKKiAh0dHQCAkJAQXL16FadOncJTTz3lVF0/Pz8UFRWhtrbW8dj58+fx8MMPo7m5GZcuXXJsdvnn\nP//ZEXK9Xo9bt24JV+25eAXSWFhYGGpqarB48WL4+/sjMDAQOp0OCxcuRHJyMiIiIuDv7485c+bg\n22+/dZwXHh6O5uZmGAwGp+pOnDgRb7zxBtavXw+r1QqdTofp06cjMzMTI0eORHR0NBYtWoQxY8Y4\n9rBob2/HY489hvT0dIwbNw7x8fED9W3wGNwTYRBQFAWJiYnYsGEDN2JxM07dwtntdmRmZiI2NhYJ\nCQm4fPlyl/GysjJERUUhNjYWpaWljseLi4sRGxuLyMhIzf54ONhdu3YNs2bNwvTp0xked+TMr+6O\nHj2qZmRkqKqqqtXV1arJZHKMKYqihoeHqz/88IOqKIoaGRmpNjQ0qJWVleqKFStUVVXV1tZWddu2\nbbLfHxK5Aad+Bjpz5gxmz54NAJg2bRpqamocY3V1dTAajY7tfWfMmIGqqip8+eWXCAoKwsqVK2G1\nWrFu3boBiD+RazkVIKvVipEjRzq+9vLygt1ux5AhQ2C1Wrvtjd3S0oLr16/jypUrKC4uxjfffAOT\nyYQjR47IV0DkQk4F6Of7X98ODwD4+vp22xvbz88Po0ePRmBgIPR6PSZOnAhvb280NTV16y3Umco/\n4pGbcypAwcHBKC8vx8KFC1FdXY2goCDHWGBgIC5duoTm5mYMHz4cVVVVSEpKgre3N3bv3o0XX3wR\nV69eRVtbG/z9/Xuto9PpUF/f0usxUuPG+d71GlrV8aS1aFVHunm9UwEKDw9HRUUFYmNjAQCbNm3C\nBx98gJs3b2LJkiXIyMhAUlIS7HY7oqKiMH78eIwfPx5VVVWIioqC3W7Hxo0beXWhQc/t/w7EV233\nquFpdaRXIL6Vh0iAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiA\nASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiAASIS0LQ7AwA0NjZi7ty5uHjxovOzJnITTgXo\n448/hs1mw/79+7F27Vrk5uY6xmw2G3Jzc7Fz506YzWZYLBY0NjY6xjIzMzF8+PCBmT2RizkVoL52\nZzAYDI7uDACwZcsWxMXFdWuSSzRYadad4b333kNAQABmzZqF4uJi9HVDVOnOke5SQ6s6nrQWLes4\nS7PuDGazGTqdDp988gnOnTuHjIwMFBUVYezYsb3W4na47lXD0+q4ZHN5Z7ozzJ8/33FMQkICsrOz\nfzE8RO5Os+4MRJ6I3Rk87HbEU9aiVR12ZyByIQaISIABIhJggIgEGCAiAQaISIABIhJggIgEGCAi\nAQaISIABIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIABIhJggIgEGCAiAae2tbLb7cjKysL5\n8+dhMBjw+uuvw2g0OsbLyspQVFQEvV6PyMhIREdHw2azYf369bhy5QoURYHJZEJYWNiALYTIFZwK\nUOfN5c+ePYvc3FwUFRUB+Nvm8gcOHMCwYcMQFxeHsLAwnDhxAgEBAdi6dSuam5sRERHBANGg51SA\n+rq5PADH5vILFixw7E5qt9vh5eUlnTuRy2m2ufyIESMc565evRopKSnCqRO5niaby48aNQoA8N13\n32HVqlWIj4/HokWL+lSLHQ3cr4Yn1nGWZpvLNzQ0IDExERs3bsSTTz7Z51rcDte9anhaHZd0Z3Bm\nc/mcnBy0tLSgsLAQhYWFAIB33nkH3t7eogUQuRI3l/ewV1NPWYtWdbi5PJELMUBEAgwQkQADRCTA\nABEJMEBEAgwQkQADRCTAABEJMEBEAgwQkQADRCTAABEJMEBEAgwQkQADRCTAABEJMEBEAgwQkQAD\nRCTAABEJMEBEApp1Z/ilc4gGI6euQJ27M6xduxa5ubmOsdvdGXbu3Amz2QyLxYLGxsZezyEarDTr\nzlBdXd3jOUSDlVNXoJ66M9weu1N3ht7OIRqsNOnO4Ofn1+s5vWFHA/er4Yl1nKVZdwadTtfjOb3h\nftLuVcPT6gya7gx3OodosGN3Bg97NfWUtWhVh90ZiFyIASISYICIBBggIgEGiEiAASISYICIBBgg\nIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiAASIS6Pe+\ncO3t7UhLS0NTUxN8fHyQm5uLgICALseUlJTAYrFAr9fDZDIhNDQULS0tSEtLQ2trK2w2GzIyMjB9\n+vQBWwiRK/T7CrRv3z4EBQVh7969iIiIwPbt27uM19fXw2w2Y//+/dixYwfy8vKgKAp27dqFmTNn\nwmw2Y9OmTcjOzh6wRRC5Sr8DdObMGcyZMwcAMHv2bHz66addxj///HMEBwfDYDBg5MiRuP/++1Fb\nW4sXXngBMTExAIBbt27B29t7AKZP5Fq93sKVlpZi9+7dXR4bM2YMfHx8APyt80Jnra2t3bozdO7Y\nUF9fj3Xr1mHDhg0DsgAiV+o1QNHR0YiOju7yWHJysqPLwu3OC539vAtD52Nqa2uRmpqK9PR0hISE\n9GmC7GjgfjU8sY6z+v1LhODgYJw8eRJTp07FyZMnuwVh6tSpyM/Ph6Io+PHHH1FXV4dJkybhq6++\nwurVq7Ft27Y+d2YAuDe2u9XwtDqad2eIi4tDeno6li5diqFDhyIvLw8AsGvXLhiNRoSFhWHZsmVY\nunQp7HY71qxZg6FDh+LNN9+EzWZDTk4OAMDPzw+FhYWiyRO5GrszeNirqaesRas67M5A5EIMEJEA\nA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIM\nEJEAA0QkwAARCTBARAIMEJFAvwPU3t6O5ORkxMfH46WXXkJTU1O3Y0pKShAZGYmYmBgcP368y1hd\nXR1CQkKgKIrTkyZyF5p1ZwAAq9WKzZs3c2N58hiadWdQVRWZmZlYs2YNA0QeQ7PuDAUFBZg7dy4m\nT548UHMncjlNujP4+vri0KFDmDBhAt599100NDQgKSkJZrP5FyfIjgbuV8MT6zhLk+4MDz74II4d\nO+Y4JiwsDDt27OhTPe4n7V41PK3OoOnO0JlOpxNNmshdsDuDh72aespatKrD7gxELsQAEQkwQEQC\nDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkw\nQEQCDBCRAANEJMAAEQn0e1+49vZ2pKWloampCT4+PsjNzUVAQECXY0pKSmCxWKDX62EymRAaGoqO\njg5s2rQJX3zxBWw2G1555RXHHttEg5Vm3Rnef/99dHR0YN++fSgsLMSFCxcGbBFErqJZd4aKigpM\nmDABK1aswGuvvYZ58+YNzAqIXEiz7gzXr1/H5cuXUVxcjKqqKrz66qvYs2fPQK2DyCU0684wevRo\nhIaGAgAee+wxfP31132aIDsauF8NT6zjrH7fwt3uzgCgx+4Mp0+fhqIoaGlpcXRnmDFjBk6cOAEA\nOHfuHO65554BmD6Ra/V7c/n29nakp6ejvr7e0Z1hzJgxXbozlJaWwmKxwG63w2QyITw8HIqiICsr\nC3V1dQCArKwsPPTQQ3dlUURacfvuDETujH9IJRJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIAB\nIhJggIgEGCAigX5/pJsGTnZ2NqZMmYJnn332rtf67LPPkJ+fj7a2NnR0dGDu3LlITU3FkCF8DZXg\nd8+FdDqdJnUURUFqairy8vLw/vvv4+DBg6irq8PevXs1qe/JeAXS2ObNm1FWVoaxY8fCYDBgypQp\nAID8/HxUVlbixo0b8Pf3R0FBAcrLy1FZWYm8vDwAQEFBAby9vbF8+XLH8+Xn5zs+Z3XbM888g8TE\nRMfXbW1taG1txc2bNwEABoMBGzZsQFtbGwAgISEBycnJePzxx/HXv/4Vy5YtQ1lZGTIyMuDr64sv\nvvgC33//PVatWoXnnnvurn5/BhsGSENHjx5FTU0NDh8+DKvV6rh1u3z5Mi5evAiLxQIASE9Px6FD\nhxATE4O33noLbW1tGDZsGA4dOtTtY/ApKSlISUnpte6oUaOwYsUKPPvsswgMDMQTTzyBBQsWYMaM\nGY5jeroaXr16FX/84x9x/vx5JCQkMEA/wwBp6NSpU5g/fz68vLwwatQozJs3D6qqwmg0Ij09HRaL\nBRcvXkR1dTWMRiNGjBiBOXPm4OjRo7j33nthNBoxbty4Ls95pyvQ4sWLkZSU1OWxl19+GbGxsaio\nqEBFRQWWL1+O1atX4/nnn+9xvjqdDr/97W8BAJMmTUJzc/MAfSc8BwOkIZ1OB7vd7vhar//p219T\nU4PU1FQkJiZiwYIF8PLywu3POUZGRmL79u2477777vjq35cr0NmzZ1FTU4P4+HgsWrQIixYtwtNP\nP4033ngDzz//PHQ6naPerVu3upw7dOhQx9ypO/4SQUMzZ87E4cOHoSgKrFYrysvLAQCnT5/GE088\ngZiYGDzwwAOoqKhAR0cHACAkJARXr17FqVOn8NRTTzlV18/PD0VFRaitrXU8dv78eTz88MMAAH9/\nf/zlL38BAHz88ceOY/hh5V/GK5CGwsLCUFNTg8WLF8Pf3x+BgYHQ6XRYuHAhkpOTERERAX9/f8yZ\nMwfffvut47zw8HA0NzfDYDA4VXfixIl44403sH79elitVuh0OkyfPh2ZmZkAgH/5l39BRkYGDhw4\ngHnz5jmuNjqdrsuVh1eh7rgngptTFAWJiYnYsGEDN2FxQ07dwtntdmRmZiI2NhYJCQm4fPlyl/Gy\nsjJERUUhNjYWpaWljseLi4sRGxuLyMhIHDx4UDbzX4Fr165h1qxZmD59OsPjrlQnHD16VM3IyFBV\nVVWrq6tVk8nkGFMURQ0PD1d/+OEHVVEUNTIyUm1oaFArKyvVFStWqKqqqq2treq2bducKU3kVpz6\nGejMmTOYPXs2AGDatGmoqalxjNXV1cFoNDq2950xYwaqqqrw5ZdfIigoCCtXroTVasW6desGIP5E\nruVUgKxWK0aOHOn42svLC3a7HUOGDIHVau22N3ZLSwuuX7+OK1euoLi4GN988w1MJhOOHDnSax1V\nVfmDK7k1pwL08/2vb4cHAHx9fbvtje3n54fRo0cjMDAQer0eEydOhLe3N5qamrr1FupMp9Ohvr6l\nx/GBMG6c712voVUdT1qLVnWke2879UuEzvtjV1dXIygoyDEWGBiIS5cuobm5GYqioKqqCo8++ihm\nzJiBP/3pTwB+entIW1sb/P39RZMncjWnrkDh4eGoqKhAbGwsAGDTpk344IMPcPPmTSxZsgQZGRlI\nSkqC3W5HVFQUxo8fj/Hjx6OqqgpRUVGw2+3YuHEjb89o0HP7vwPxtse9anhaHZfcwhHRTxggIgEG\niEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBgg\nIgEGiEjVRhksAAAa20lEQVSAASISYICIBDTdXB4AGhsbMXfuXFy8eNH5WRO5CacC9PHHH8Nms2H/\n/v1Yu3YtcnNzHWM2mw25ubnYuXMnzGYzLBYLGhsbHWOZmZkYPnz4wMyeyMWcClBfN5c3GAyOzeUB\nYMuWLYiLi+vW55NosHIqQD1tLn977E6by7/33nsICAjArFmzALB9IHkGzTaXN5vN0Ol0+OSTT3Du\n3DlkZGSgqKgIY8eO7bWWdOfIvtCihlZ1PGktWtZxllMBCg4ORnl5ORYuXNjr5vLDhw9HVVUVkpKS\nMH/+fMcxCQkJyM7O/sXwANza191qeFodaUA121yeyBNxc3kPezX1lLVoVYebyxO5EANEJMAAEQkw\nQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAA\nEQkwQEQCDBCRAANEJODUvnB2ux1ZWVk4f/48DAYDXn/9dRiNRsd4WVkZioqKoNfrERkZiejoaNhs\nNqxfvx5XrlyBoigwmUwICwsbsIUQuYJTAercneHs2bPIzc1FUVERgL91Zzhw4ACGDRuGuLg4hIWF\n4cSJEwgICMDWrVvR3NyMiIgIBogGPacC1NfuDAAc3RkWLFjg2N7XbrfDy8tLOncil3MqQD11Zxgy\nZEiP3RlGjBjhOHf16tVISUkRTp3I9TTpzjBq1CgAwHfffYdVq1YhPj4eixYt6lMtdjRwvxqeWMdZ\nmnVnaGhoQGJiIjZu3Ignn3yyz7W4n7R71fC0OoOmO0NOTg5aWlpQWFiIwsJCAMA777wDb29v0QKI\nXIndGTzs1dRT1qJVHXZnIHIhBohIgAEiEmCAiAQYICIBBohIgAEiEmCAiAQYICIBBohIgAEiEmCA\niAQYICIBBohIgAEiEmCAiAQYICIBBohIgAEiEmCAiAQYICIBBohIQLPuDL90DtFg5NQVqHN3hrVr\n1yI3N9cxdrs7w86dO2E2m2GxWNDY2NjrOUSDlWbdGaqrq3s8h2iwcuoK1FN3httjd+rO0Ns5RIOV\nJt0Z/Pz8ej2nN+xo4H41PLGOszTrzqDT6Xo8pzfcT9q9anhanUHTneFO5xANduzO4GGvpp6yFq3q\nsDsDkQsxQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQCDBCR\nAANEJMAAEQkwQEQCDBCRAANEJMAAEQkwQEQC/d4Xrr29HWlpaWhqaoKPjw9yc3MREBDQ5ZiSkhJY\nLBbo9XqYTCaEhoaipaUFaWlpaG1thc1mQ0ZGBqZPnz5gCyFyhX5fgfbt24egoCDs3bsXERER2L59\ne5fx+vp6mM1m7N+/Hzt27EBeXh4URcGuXbswc+ZMmM1mbNq0CdnZ2QO2CCJX6XeAzpw5gzlz5gAA\nZs+ejU8//bTL+Oeff47g4GAYDAaMHDkS999/P2pra/HCCy8gJiYGAHDr1i14e3sPwPSJXKvXW7jS\n0lLs3r27y2NjxoyBj48PgL91XuistbW1W3eGzh0b6uvrsW7dOmzYsGFAFkDkSr0GKDo6GtHR0V0e\nS05OdnRZuN15obOfd2HofExtbS1SU1ORnp6OkJCQPk2QHQ3cr4Yn1nFWv3+JEBwcjJMnT2Lq1Kk4\nefJktyBMnToV+fn5UBQFP/74I+rq6jBp0iR89dVXWL16NbZt29bnzgwA98Z2txqeVkfz7gxxcXFI\nT0/H0qVLMXToUOTl5QEAdu3aBaPRiLCwMCxbtgxLly6F3W7HmjVrMHToULz55puw2WzIyckBAPj5\n+aGwsFA0eSJXY3cGD3s19ZS1aFWH3RmIXIgBIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIAB\nIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIABIhLod4Da29uRnJyM\n+Ph4vPTSS2hqaup2TElJCSIjIxETE4Pjx493Gaurq0NISAgURXF60kTuQrPuDABgtVqxefNmbixP\nHkOz7gyqqiIzMxNr1qxhgMhjaNadoaCgAHPnzsXkyZMHau5ELqdJdwZfX18cOnQIEyZMwLvvvouG\nhgYkJSXBbDb/4gTZ0cD9anhiHWdp0p3hwQcfxLFjxxzHhIWFYceOHX2qx/2k3auGp9UZNN0ZOtPp\ndKJJE7kLdmfwsFdTT1mLVnXYnYHIhRggIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEG\niEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIoF+7wvX3t6OtLQ0NDU1\nwcfHB7m5uQgICOhyTElJCSwWC/R6PUwmE0JDQ9HR0YFNmzbhiy++gM1mwyuvvOLYY5tosNKsO8P7\n77+Pjo4O7Nu3D4WFhbhw4cKALYLIVTTrzlBRUYEJEyZgxYoVeO211zBv3ryBWQGRC2nWneH69eu4\nfPkyiouLUVVVhVdffRV79uwZqHUQuYRm3RlGjx6N0NBQAMBjjz2Gr7/+uk8TZEcD96vhiXWc1e9b\nuNvdGQD02J3h9OnTUBQFLS0tju4MM2bMwIkTJwAA586dwz333DMA0ydyrX5vLt/e3o709HTU19c7\nujOMGTOmS3eG0tJSWCwW2O12mEwmhIeHQ1EUZGVloa6uDgCQlZWFhx566K4sikgrbt+dgcid8Q+p\nRAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIMEJEAA0Qk0O+PdJNcdnY2pkyZgmefffau\n1lm1ahX++te/4ubNm6ivr8f9998PAFi7di0++OADPPHEE3d9Dp6OAXIBnU6nSZ2CggIAwKlTp/D2\n22/DbDY7xj788ENN5uDpGCCNbN68GWVlZRg7diwMBgOmTJkCAMjPz0dlZSVu3LgBf39/FBQUoLy8\nHJWVlcjLywPwUxC8vb2xfPlyx/Pl5+c7Pl912zPPPIPExMRutXt6w/3x48fxxz/+EY2NjXj55Zex\nZMkSvP3229DpdFi1ahUAICwsDGazGZ999hkOHjyIGzduICwsDCkpKQPyfRnsGCANHD16FDU1NTh8\n+DCsVqvjtuny5cu4ePEiLBYLACA9PR2HDh1CTEwM3nrrLbS1tWHYsGE4dOhQt4+/p6SkiP4nVlUV\niqKgtLQUf/nLX7Bs2TIsWbLkjlfH249du3YNH330EYYM4Y/OtzFAGjh16hTmz58PLy8vjBo1CvPm\nzYOqqjAajUhPT4fFYsHFixdRXV0No9GIESNGYM6cOTh69CjuvfdeGI1GjBs3rstz3ukKtHjxYiQl\nJfVpTjqdzrGxyz/8wz/g+vXrAH4K1s9DdPsK9vDDDzM8P8MAaUCn08Futzu+1ut/+rbX1NQgNTUV\niYmJWLBgAby8vBz/s0ZGRmL79u2477778Nxzz3V7TukVCAC8vLwc8+s81863fLdu3XL897Bhw0T1\nPBFfTjQwc+ZMHD58GIqiwGq1ory8HABw+vRpPPHEE4iJicEDDzyAiooKdHR0AABCQkJw9epVnDp1\nCk899ZRmc/X398dXX30F4Kctyurr6zX7pcdgxCuQBsLCwlBTU4PFixfD398fgYGB0Ol0WLhwIZKT\nkxEREQF/f3/MmTMH3377reO88PBwNDc3w2AwiOrrdLpef7bp/N+LFi3CsWPHsGjRIjzyyCN4+OGH\n73hbRz9xak8Eu92OrKwsnD9/HgaDAa+//jqMRqNjvKysDEVFRdDr9YiMjHRsjVVcXIzy8nLYbDb8\n/ve/598geqEoChITE7FhwwZuvuLOVCccPXpUzcjIUFVVVaurq1WTyeQYUxRFDQ8PV3/44QdVURQ1\nMjJSbWhoUCsrK9UVK1aoqqqqra2t6rZt25wp/atw9epV9bHHHlO3bt3q6qnQL3DqFu7MmTOYPXs2\nAGDatGmoqalxjNXV1cFoNDp2J50xYwaqqqrw5ZdfIigoCCtXroTVasW6desGIP6eafz48Th16pSr\np0F94FSArFYrRo4c6fjay8sLdrsdQ4YMgdVq7ba1b0tLC65fv44rV66guLgY33zzDUwmE44cOSJf\nAZELORWgn2/fezs8AODr69tta18/Pz+MHj0agYGB0Ov1mDhxIry9vdHU1NStNUpnKn94JTfnVICC\ng4NRXl6OhQsXorq6GkFBQY6xwMBAXLp0Cc3NzRg+fDiqqqqQlJQEb29v7N69Gy+++CKuXr2KtrY2\n+Pv791pHp9Ohvr6l12Okxo3zves1tKrjSWvRqo50722nAhQeHo6KigrExsYCADZt2oQPPvgAN2/e\nxJIlS5CRkYGkpCTY7XZERUVh/PjxGD9+PKqqqhAVFQW73Y6NGzfy6kKDnttv7ctXbfeq4Wl1pFcg\nvhOBSIABIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIABIhJggIgE\nGCAiAQaISIABIhJggIgEGCAiAQaISIABIhJwKkB2ux2ZmZmIjY1FQkICLl++3GW8rKwMUVFRiI2N\nRWlpaZexxsZGzJ07FxcvXnR+1kRuwqkAffzxx7DZbNi/fz/Wrl2L3Nxcx5jNZkNubi527twJs9kM\ni8WCxsZGx1hmZiaGDx8+MLMncjGnAtTX7gwGg8HRnQEAtmzZgri4uG79PokGK6cC1FN3httjd+rO\n8N577yEgIACzZs0C0HPrdaLBRLPuDGazGTqdDp988gnOnTuHjIwMFBUVYezYsb3Wkm692hda1NCq\njietRcs6ztKsO8P8+fMdxyQkJCA7O/sXwwNwb2x3q+FpdQZNdwYiT8TuDB72auopa9GqDrszELkQ\nA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIM\nEJEAA0QkwAARCTBARAIMEJEAA0Qk4NS+cHa7HVlZWTh//jwMBgNef/11GI1Gx3hZWRmKioqg1+sR\nGRmJ6Oho2Gw2rF+/HleuXIGiKDCZTAgLCxuwhRC5glMB6tyd4ezZs8jNzUVRURGAv3VnOHDgAIYN\nG4a4uDiEhYXhxIkTCAgIwNatW9Hc3IyIiAgGiAY9pwLU1+4MABzdGRYsWODY3tdut8PLy0s6dyKX\ncypAPXVnGDJkSI/dGUaMGOE4d/Xq1UhJSRFOncj1NOnOMGrUKADAd999h1WrViE+Ph6LFi3qUy12\nNHC/Gp5Yx1madWdoaGhAYmIiNm7ciCeffLLPtbiftHvV8LQ6g6Y7Q05ODlpaWlBYWIjCwkIAwDvv\nvANvb2/RAohcid0ZPOzV1FPWolUddmcgciEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEG\niEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEiAASISYICIBBggIgEGiEhAs+4Mv3QO0WDk\n1BWoc3eGtWvXIjc31zF2uzvDzp07YTabYbFY0NjY2Os5RIOVZt0ZqqurezyHaLBy6grUU3eG22N3\n6s7Q2zlEg5Um3Rn8/Px6Pac37GjgfjU8sY6zNOvOoNPpejynN9xP2r1qeFqdQdOd4U7nEA127M7g\nYa+mnrIWreqwOwORCzFARAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAAR\nCTBARAIMEJEAA0QkwAARCTBARAIMEJEAA0QkwAARCTBARAL93heuvb0daWlpaGpqgo+PD3JzcxEQ\nENDlmJKSElgsFuj1ephMJoSGhqKlpQVpaWlobW2FzWZDRkYGpk+fPmALIXKFfl+B9u3bh6CgIOzd\nuxcRERHYvn17l/H6+nqYzWbs378fO3bsQF5eHhRFwa5duzBz5kyYzWZs2rQJ2dnZA7YIIlfpd4DO\nnDmDOXPmAABmz56NTz/9tMv4559/juDgYBgMBowcORL3338/amtr8cILLyAmJgYAcOvWLXh7ew/A\n9Ilcq9dbuNLSUuzevbvLY2PGjIGPjw+Av3Ve6Ky1tbVbd4bOHRvq6+uxbt06bNiwYUAWQORKvQYo\nOjoa0dHRXR5LTk52dFm43Xmhs593Yeh8TG1tLVJTU5Geno6QkJA+TZAdDdyvhifWcVa/f4kQHByM\nkydPYurUqTh58mS3IEydOhX5+flQFAU//vgj6urqMGnSJHz11VdYvXo1tm3b1ufODAD3xna3Gp5W\nR/PuDHFxcUhPT8fSpUsxdOhQ5OXlAQB27doFo9GIsLAwLFu2DEuXLoXdbseaNWswdOhQvPnmm7DZ\nbMjJyQEA+Pn5obCwUDR5IldjdwYPezX1lLVoVYfdGYhciAEiEmCAiAQYICIBBohIgAEiEmCAiAQY\nICIBBohIgAEiEmCAiAQYICIBBohIgAEiEmCAiAQYICIBBohIgAEiEmCAiAQYICIBBohIgAEiEuh3\ngNrb25GcnIz4+Hi89NJLaGpq6nZMSUkJIiMjERMTg+PHj3cZq6urQ0hICBRFcXrSRO5Cs+4MAGC1\nWrF582ZuLE8eQ7PuDKqqIjMzE2vWrGGAyGNo1p2hoKAAc+fOxeTJkwdq7kQup0l3Bl9fXxw6dAgT\nJkzAu+++i4aGBiQlJcFsNv/iBNnRwP1qeGIdZ2nSneHBBx/EsWPHHMeEhYVhx44dfarH/aTdq4an\n1Rk03Rk60+l0okkTuQt2Z/CwV1NPWYtWddidgciFGCAiAQaISIABIhJggIgEGCAiAQaISIABIhJg\ngIgEGCAiAQaISIABIhJggIgEGCAiAQaISIABIhJggIgEGCAiAQaISIABIhJggIgEGCAigX7vC9fe\n3o60tDQ0NTXBx8cHubm5CAgI6HJMSUkJLBYL9Ho9TCYTQkND0dHRgU2bNuGLL76AzWbDK6+84thj\nm2iw0qw7w/vvv4+Ojg7s27cPhYWFuHDhwoAtgshVNOvOUFFRgQkTJmDFihV47bXXMG/evIFZAZEL\nadad4fr167h8+TKKi4tRVVWFV199FXv27BmodRC5hGbdGUaPHo3Q0FAAwGOPPYavv/66TxNkRwP3\nq+GJdZzV71u4290ZAPTYneH06dNQFAUtLS2O7gwzZszAiRMnAADnzp3DPffcMwDTJ3Ktfm8u397e\njvT0dNTX1zu6M4wZM6ZLd4bS0lJYLBbY7XaYTCaEh4dDURRkZWWhrq4OAJCVlYWHHnroriyKSCtu\n352ByJ3xD6lEAgwQkQADRCTg0gC1t7cjOTkZ8fHxeOmll9DU1NTtmJKSEkRGRiImJgbHjx8HAHR0\ndCAnJwdxcXGIiopy/FZwoOvcVldXh5CQECiKclfqtLS04OWXX0ZCQgJiY2NRXV3d7Ty73Y7MzEzE\nxsYiISEBly9f7jJeVlaGqKgoxMbGorS0tE/n3IkzdWw2G9LS0hAfH4/o6GiUlZUNeI3bGhsbMXfu\nXFy8ePGurAUAiouLERsbi8jISBw8eLD3IqoL/eEPf1DffvttVVVV9cMPP1RzcnK6jF+7dk19+umn\nVUVR1JaWFvXpp59Wf/zxR/XAgQNqVlaWqqqq+v3336s7d+68K3VUVVVbWlrU5cuXqzNnznQ8NtB1\n/uM//kP9r//6L1VVVfXChQvqs88+2+25jx49qmZkZKiqqqrV1dWqyWRyjCmKooaHh6s//PCDqiiK\nGhkZqTY0NPR6Tk+cqXPgwAH1jTfeUFVVVW/cuKGGhoYOeI3bYytXrlTnz5+vXrhw4a6spbKyUl2x\nYoWqqqra2tqqbtu2rdcaLr0CafW2IGfrqKqKzMxMrFmzBt7e3ndtPS+88AJiYmIAALdu3bpjrTNn\nzmD27NkAgGnTpqGmpsYxVldXB6PRCF9fXxgMBsyYMQNVVVW9ntPbGvpbZ8GCBXjllVcA/PSq7+Xl\nNeA1AGDLli2Ii4vDuHHjfnEdztapqKhAUFAQVq5ciZdffhlhYWG91uj3u7GdpdXbggayTkFBAebO\nnYvJkyff1fXcfqy+vh7r1q3Dhg0butWzWq0YOXKk42svLy/Y7XYMGTKky3N0rt3bOT1xps6IESMc\n565evRopKSk9Pr+zNd577z0EBARg1qxZKC4uhtqHv744U+f69eu4cuUKiouL8c0338BkMuHIkSM9\n1tAsQFq9LWgg6xw6dAgTJkzAu+++i4aGBiQlJcFsNg9ondvH1NbWIjU1Fenp6d3e3XGnczsHwdfX\n947P29s5PelvnVGjRgEAvvvuO6xatQrx8fFYtGjRgNbw8/OD2WyGTqfDJ598gnPnziEjIwNFRUUY\nO3bsgNYZPXo0AgMDodfrMXHiRHh7e6OpqanbR3Zuc+ktnFZvC3K2zrFjx2A2m2E2mzF27Fjs2LFj\nwOtMmjQJX331FVavXo28vDzHLUdvz11dXY2goCDHWGBgIC5duoTm5mYoioKqqio8+uijvZ7TlzX0\npc706dPR0NCAxMREpKWl4bnnnhvwGo8++ij27Nnj+LeYPHkyNm/e3Gt4nK0zY8YM/OlPfwIAXL16\nFW1tbfD39++xhkvfiaDV24KcrdPZvHnz8NFHH2Ho0KEDXmflypWora11vBD4+fmhsLCwy3Orqoqs\nrCzU1tYCgOPDiTdv3sSSJUtQXl6OwsJC2O12REVFYenSpXc8Z+LEib3+mzhTJycnB0eOHOny3O+8\n806PPzc6U6OzhIQEZGdn35W1AMDWrVvx2WefwW63IzU1Fb/97W97rMG38hAJ8A+pRAIMEJEAA0Qk\nwAARCTBARAIMEJEAAzRItbS04F//9V9x7do1vPTSS66ezq8WAzRINTc343//938xfvx4/Od//qer\np/OrxT+kDlIvv/wy/vznPyM0NBRffvklysrKkJGRAYPBgC+//BJWqxUrV67E7373O1dP1aPxCjRI\nvfbaaxg/fjxeffXVLo9fu3bN8U7xLVu2oKGhwUUz/HVggAapO9046HQ6REdHY8iQIZgwYQKCg4Px\nP//zPy6Y3a8HA+RhOn+YzW63Q6/X7BMrv0oM0CCl1+vR0dHR5Uqkqio++OADAMC3336Ls2fP3vGz\nRTRw+PI0SI0dOxZ/93d/h/Xr10On0wH46Rbu5s2beO6552Cz2ZCTk+P4wBvdHQzQIKXX67F///5u\njy9evBj//M//7IIZ/TrxFo5IgH8HIhLgFYhIgAEiEmCAiAQYICIBBohIgAEiEvg/PvLJbs3hIvkA\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1137b1a50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = sns.FacetGrid(tips, col=\"day\", col_wrap=3, aspect=1)\n",
      "g.map(plt.hist, \"tip\");\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAGqCAYAAACbEvXuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9wVPW9//HXZjcSQ340kcS2AgnkUolFcRBQejE6CBhF\nRFGUGFMuQSsIiIAMIQkEKb+Uq9IxuQVx5nYaUsI411+xWO5XQFMBTVtrOhAUHQgIYn6QmNn80CzZ\n/f7BkCuSH5vN7jkbzvPxF9ndc97vT3Y/7Cvn7GePzePxeAQAAADLCDG7AQAAABiLAAgAAGAxBEAA\nAACLIQACAABYDAEQAADAYgiAAAAAFuMwuwF0bM2aNbr++ut1//33B7zW8OHDNXz48Evq33DDDRfd\nlpubq7S0NP3yl78MeE9ATxk5Zz7++GO99NJLamlpUVtbm2677TYtXbpUISGd/029d+9enTx5Uv/x\nH/8R8P6Angj2uYPAIAAGKZvNZmi9N998s9vHrF271oBOAN8YNWdaW1u1dOlS7dy5U9dcc41cLpcW\nLlyooqIiZWRkdLrd4cOHDZ/XgDeCfe4gMAiAQeS5557T3r17NWDAAIWGhur666+XJL300kv66KOP\n9O233yomJkb5+fnat2+fPvroI73wwguSpPz8fPXr10+PP/54+/5eeuklffDBBxfVuPfee5WZmelV\nPy+//LI+/fRTffPNN0pPT9e7776rhQsXauzYsX4aMdA7ZsyZlpYWNTU1qbm5WZIUGhqqnJwctbS0\nSJLKysq0efNmfffdd2poaNCyZcs0bNgwFRcXy2az6ZprrjHkSAvQlWCcOxkZGe3vMadOndKvf/1r\n7d27V1lZWYqMjNThw4f1zTffaMGCBZo+fXpAfz9WQAAMErt379ahQ4e0a9cuNTY2tr9BnDx5UseP\nH9fOnTslScuXL1dJSYkefvhhbd68WS0tLQoLC1NJSYm2b99+0T4XL16sxYsXe1X/vvvua//3Lbfc\noqysLEmSy+XSn//8Z0nSu+++yxEMBA2z5kx0dLSeeOIJ3X///Ro6dKhuvvlmpaam6qabbpIkbd++\nXevWrdOQIUN08OBBrV+/XiUlJUpLS5Mkwh9MF6xzR+r8aGRVVZX+9Kc/6ejRo8rIyCAA+gEBMEiU\nlZXpzjvvlN1uV3R0tO644w55PB4NHjxYy5cv186dO3X8+HF9+umnGjx4sMLDw5WSkqLdu3dr4MCB\nGjx4sOLi4i7aZ0d/kU2dOlVz5sy5pH5Hp4BtNptGjhzp34ECfmLmnJk7d65mzpyp/fv3a//+/Xr8\n8ce1aNEizZo1S//5n/+pvXv36t1331V5eXn70Q2Px8MfUAgKwTp3OmOz2fTv//7vkqRhw4apoaHB\nT78JayMABgmbzSa3293+s8Nx/qk5dOiQli5dqszMTKWmpsput+vC5ZsfeOAB/f73v9egQYM6/Guo\nJ0cAO9OvX79ebQ8Eillzpry8XIcOHVJ6erqmTJmiKVOm6J577tH69es1a9YspaWlady4cRo7dqzG\njRunpUuX+nHUQO8F69yx2Wzt9c6dO3fRtldccUV77/APlt0EiV/96lfatWuXWltb1djYqH379kmS\n/v73v+vmm2/Www8/rKSkJO3fv19tbW2SpNGjR6uqqkplZWWaOHGime0DhjNrzkRFRem//uu/9Pnn\nn7ffdvToUV133XVqaGjQiRMn9NRTTyklJUUffvhh+xutw+G45E0NMEMwzh1JiomJ0RdffCFJeu+9\n99ofcyEUwr84AhgkJkyYoEOHDmnq1KmKiYnR0KFDZbPZdNddd2nhwoW67777FBMTo5SUFJ0+fbp9\nu0mTJqmhoUGhoaE+1+YvKvRFZs2ZIUOGaP369crOzlZjY6NsNptuvPFGrVq1ShEREZoxY4amTJmi\nq666SpMmTVJra6u+++47jRkzRsuXL1dcXJzS09P99WsAeiwY544kPfbYY8rKytL//M//6I477mh/\nb7LZbBe9T/Ge5R82D9G6z2ptbVVmZqZycnKUnJxsdjtA0GPOAL5h7lx+ujwF3NbWphUrVigtLU2P\nPPKIvvjiC504cUJpaWlKT0/X6tWrOTRrkurqao0fP1433ngjkxHwAnMG8A1z5/LU5RHA9957T/v2\n7dO6detUVlam//7v/5YkZWZmasyYMcrLy9Ott97K588AAAD6kC6PAE6cOFFr1qyRJJ0+fVrR0dE6\nfPiwxowZI0lKSUnRgQMHAt8lAAAA/KbbVcB2u11ZWVlat26dpk6detEp3/DwcDmdzoA2CAAAAP/y\nahXwxo0bVVtbqxkzZqi1tbX99qamJkVFRXW7PV+ACviGuQP4hrkDdK3LAPjmm2+qqqpKTzzxhMLC\nwhQSEqIRI0aorKxMY8eOVWlpqcaNG9dtEZvNppoac44UxsVFmlbb7PqM3dyx+4OZc0fiOWTs5tT2\nByvPHbPrM/a+8d7TZQBMTU1VVlaWHn30UZ07d045OTkaOnSoVq5cKZfLpaSkJKWmpva6YQAAABin\nywAYFhamzZs3X3J7YWFhwBoCAABAYHEpOAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIg\nAACAxXh1JRAA1tHW1qbKymOSpPr6CNXVNXq9bWLiUNnt9kC1BgDwEwIggItUVh7Tok1vKzw6vkfb\nNTdU63fL7lVS0rAAdQYA8BcCIIBLhEfHKyLmGrPbAAAECJ8BBAAAsBgCIAAAgMUQAAEAACyGAAgA\nAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMV0eSk4l8ul7Oxsff3112pt\nbdW8efP005/+VE888YQSExMlSWlpabr77ruN6BUAAAB+0GUALCkpUWxsrDZt2qSGhgZNmzZN8+fP\nV2ZmpmbPnm1UjwAAAPCjLgNgamqq7rzzTkmS2+2Ww+HQ4cOHdfz4ce3Zs0cJCQnKzs5W//79DWkW\nAAAAvddlAAwPD5ckNTY2atGiRVq8eLG+//57PfTQQ7ruuuu0ZcsW5efna/ny5YY0CyB4edxunTx5\nwqdtExOHym63+7kjAEBnbB6Px9PVA86cOaMFCxYoPT1d06dPl9PpVGRkpCTpyy+/1Nq1a/WHP/zB\niF4BGODo0aN6YuN7ioi5pkfbVVd+Ismm8Oj4Hm3X3FCtwg2P6Be/+EWPtgMA+K7LI4C1tbXKzMxU\nXl6ebrnlFknSY489ppycHN1www06ePCgRowY4VWhmhpn77v1QVxcpGm1za7P2M0du78YPY66ukaf\ntw2Pju9xcLxQ88fjDIbn0MqvXzPH7i9W/R2aXZ+x9433ni4D4JYtW+R0OlVQUKCCggJJUnZ2tjZs\n2CCHw6H4+HitWbOmd90CAADAUF0GwNzcXOXm5l5y+44dOwLWEAAAAAKLL4IGAACwGAIgAACAxRAA\nAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYA\nCAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALMbR1Z0ul0vZ\n2dn6+uuv1draqnnz5ikpKUlZWVkKCQnRsGHDlJeXJ5vNZlS/AAAA6KUuA2BJSYliY2O1adMmNTQ0\naNq0aUpOTtaSJUs0ZswY5eXlac+ePZo4caJR/QIAAKCXujwFnJqaqqeeekqS5Ha75XA4VFFRoTFj\nxkiSUlJSdODAgcB3CQAAAL/pMgCGh4erf//+amxs1KJFi/T000/L7XZfdL/T6Qx4kwAAAPCfLk8B\nS9KZM2e0YMECpaen65577tGmTZva72tqalJUVJRXheLiIn3vspfMrG12fcbe9xk9jvr6CEPrSVJs\nbESH4zT7ObTy69fs+v5g9hisXJ+xB78uA2Btba0yMzOVl5enW265RZKUnJyssrIyjR07VqWlpRo3\nbpxXhWpqzDlSGBcXaVpts+szdnPH7i9Gj6OurtHQehdq/nicwfAcWvn1a+bY/cWqv0Oz6zP2vvHe\n02UA3LJli5xOpwoKClRQUCBJysnJ0bp16+RyuZSUlKTU1NTedQsAAABDdRkAc3NzlZube8nthYWF\nAWsIAAAAgcUXQQMAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAA\nAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGIfZDQAA0Ne0tbWpsvJYp/fX10eorq5RiYlDZbfb\nDewM8A4BEACAHqqsPKZFm95WeHR8p49pbqjW75bdq6SkYQZ2BniHAAgAgA/Co+MVEXON2W0APuEz\ngAAAABZDAAQAALAYTgEDl6nm5madOXO6x9udPHkiAN0A8EV3i00uYLEJeooACFym3ih5V2/9o7nH\n29WfOaqYn/8iAB0B6CkWmyBQCIDA5cpmU0Rszz+g/n1TXQCaAeArFpsgELz6DGB5ebkyMjIkSRUV\nFUpJSVFGRoYyMjK0a9eugDYIAAAA/+r2COC2bdv09ttvq3///pKkw4cPa/bs2Zo9e3bAmwMAAID/\ndXsEMCEhQfn5+fJ4PJKkQ4cO6f3339ejjz6qnJwcNTU1BbxJAAAA+E+3RwAnT56sU6dOtf88cuRI\nPfzww7ruuuu0ZcsW5efna/ny5d0WiouL7F2nvWBmbbPrM/a+z9dxREaE+bmTwImNjehwnGY/h1Z+\n/Zpd3x8COYb6+givHtfZa9uIOrx+rVe7J3q8CGTSpEmKjDw/uIkTJ2rt2rVebVdT4+xpKb+Ii4s0\nrbbZ9Rm7uWP3F1/H4Wz8TlLfCIF1dY2XjDMYnkMrv37NHLu/BHIMdXWNXj+uN334Wsfs59DKr1+z\nx+6tHn8R9GOPPaZ//etfkqSDBw9qxIgRPd0FAAAATOT1EUCbzSZJevbZZ/Xss8/K4XAoPj5ea9as\nCVhzAAAA8D+vAuDAgQNVXFwsSRo+fLh27NgR0KYAAAAQOFwLGAAAwGIIgAAAABZDAAQAALAYAiAA\nAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAAB\nAAAshgAIAABgMQRAAAAAiyEAAgAAWIzD7AYAALCitrY2VVYe6/IxJ0+eMKgbWA0BEAAAE1RWHtOi\nTW8rPDq+08ecPXVEVw1MNrArWAUBEAAAk4RHxysi5ppO729uqDKwG1iJV58BLC8vV0ZGhiTpxIkT\nSktLU3p6ulavXi2PxxPQBgEAAOBf3QbAbdu2KTc3Vy6XS5K0YcMGLVmyREVFRfJ4PNqzZ0/AmwQA\nAID/dBsAExISlJ+f336kr6KiQmPGjJEkpaSk6MCBA4HtEAAAAH7VbQCcPHmy7HZ7+88/POUbHh4u\np9MZmM4AAAAQED1eBBIS8n+ZsampSVFRUV5tFxcX2dNSfmNmbbPrM/a+z9dxREaE+bmTwImNjehw\nnGY/h1Z+/Zpd3x8COYb6+givHtfZa7sn+/C1Dq9f69XuiR4HwOTkZJWVlWns2LEqLS3VuHHjvNqu\npsacI4VxcZGm1Ta7PmM3d+z+4us4nI3fSeobIbCurvGScQbDc2jl16+ZY/eXQI6hrq7R68d11oe3\n+/CljtnPoZVfv2aP3VteB0CbzSZJysrK0sqVK+VyuZSUlKTU1NSedwgAAADTeBUABw4cqOLiYklS\nYmKiCgsLA9oUAAAAAodrAQMAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALKbHXwQN\nAP7kcbt18uSJS26vr4/w6otyExOHXnS5SiBYdPbavqCr+4zW1tamyspj3T6O+Xb5IAACMFWLs0Yv\n7KxVePSZHm/b3FCt3y27V0lJwwLQGdA73b22z546oqsGJhvcVccqK49p0aa3FR4d3+ljmG+XFwIg\nANOFR8crIuYas9sA/K6r13ZzQ5XB3XSNeWgtfAYQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEA\nACyGAAgAAGAxBEAAAACLIQACAABYDF8EDQBAH9bRJed+fClFLuGGHyMAAgDQh3V3yTku4YaO+BwA\n77//fkVEREiSBg0apPXr1/utKQAA4D0u44ae8ikAfv/995KkwsJCvzYDAACAwPNpEchnn32mlpYW\nzZkzR7NmzVJ5ebm/+wIAAECA+HQE8Morr9ScOXM0Y8YMVVZW6vHHH9fu3bsVEsKiYgAAgGDnUwBM\nTExUQkJC+79/8pOfqKamRldffXWn28TFRfrWoR+YWdvs+oy97/N1HJERYX7uJDjFxkYE7Lm28uvX\n7Pr+EMgx1NdHBGzfgdDdPPF2PN7ON7NfP1aeu97yKQC+/vrr+vzzz5WXl6eqqio1NjYqLi6uy21q\napw+NdhbcXGRptU2uz5jN3fs/uLrOJyN30m6/ENgXV1jQJ5rq79+zRy7vwRyDD/8ipW+oLt54u14\nvJlvVn/9mj12b/kUAB988EGtWLFC6enpkqQNGzZw+hcAAKCP8CkAOhwObdq0yd+9AAAAwAB8EXQH\n2traVFl5rMP7+DZ1IHh0dAUEbzGXAVgZAbADlZXHtGjT2wqPjr/odr5NHQgu3V0BoTPMZQBWRwDs\nBN+qDvQNzFUA6DlWbgAAAFgMARAAAMBiCIAAAAAW0yc/A2jWKt2uVhyyohAALh9//+RTfdvQ0On9\nZ74+bWA3vePNanlfV9Oj7+qTAdCsVbqdrThkRSEAXF7+8EapGuyJnd5fe6pSkVcNMq6hXvBmtfzZ\nU0d01cBkA7uC2fpkAJTMW/nHikMAuPyFhvZT6BWdXx/XEdq3LrPY3XtXc0OVgd0gGPAZQAAAAIsh\nAAIAAFhMnz0F3JGOPuhaXx+hurrGDhdpdLaYxF8fhm1ra9PRo0dVV9d40e0sGAH6pq4WoHWHeY++\nrrvFJG1tbZJsqq+PuuR974eYC8HhsgqAPV2k0dliEn99GLaj/bNgBOi7Ovs/ozvMe1wOultMcvbU\nEV0ZeVWX84O5EDwuqwAo9XyRRkeP9+eHYVk0AlxemNOwsq5e/80NVcyPPoTPAAIAAFgMARAAAMBi\nCIAAAAAWE/SfAfzr/o908vTXF91WXfWNpEiv99HZyiUzLn3D5eQA83lzaawL3yDwQ77+n+FNvY5q\nX1hVabf3/G/13mwbGzuyx9sA6FuCPgD+v4OHderckItua6xv6tE+Olu5ZMalb7icHGA+by6N1RFf\n/8/oTb3uVlX6e9vmhmoVbohQTMzPelwTQN/hUwB0u91avXq1jh49qtDQUK1bt06DBw/2d29+FejV\nvr3tBYCxfJmHvfk/w9d6vv5/wYpMAF3x6TOA7733nlwul4qLi/XMM89o48aN/u4LAAAAAeJTAPzk\nk0906623SpJGjhypQ4cO+bUpAAAABI5Pp4AbGxsVERHR/rPdbpfb7VZIiHGLipsbqi+5rcVZJ8lm\n+O3NDdWdLjL5cZ9d7QMIFr68Hjt7bQdqOzNq9pXterMt/xd5r7vfVXfPgTfPkT8eE0x1eH0FD5vH\n4/H0dKONGzdq5MiRuuuuuyRJt912mz744AO/NwcAAAD/8+mQ3ahRo1RaWipJ+vTTT3Xttdf6tSkA\nAAAEjk9HAD0ej1avXq3PP/9ckrRhwwYNGTKkm60AAAAQDHwKgAAAAOi7uBQcAACAxRAAAQAALIYA\nCAAAYDEEQAAAAIshAAJByO12a9WqVZo5c6YyMjJ08uRJw2q7XC4tW7ZM6enpmjFjhvbu3WtY7R86\ne/asbrvtNh0/ftzQulu3btXMmTP1wAMP6I033jC0ttvt1ooVK5SWlqb09HQdO3bMkLrl5eXKyMiQ\nJJ04caK9/urVq2XEOsEf1j9y5IjS09OVkZGhOXPm6OzZsz3al5lzRwqO+WPW3JHMmz/MnZ7PHQIg\nEITMvN52SUmJYmNjVVRUpFdffVW//e1vDat9gcvl0qpVq3TllVcaWvfjjz/WP//5TxUXF6uwsFBf\nffWVofU//PBDtbS0aMeOHZo/f742b94c8Jrbtm1Tbm6uXC6XpPNf67VkyRIVFRXJ4/Foz549htZf\nv369Vq5cqcLCQk2ePFnbtm3r0f7Mvla92fPHrLkjmTt/mDs9nzsEQCAImXm97dTUVD311FOSzv9V\nbbfbDat9wfPPP6+0tDTFxcUZWnf//v269tpr9eSTT2ru3LmaMGGCofXDwsLkdDrl8XjkdDoVGhoa\n8JoJCQnKz89vP1pRUVGhMWPGSJJSUlJ04MABQ+u/+OKLGj58uCTp3Llz6tevX4/2Z/a16s2eP2bN\nHcnc+cPc6fnc8elawAACy8zrbYeHh7f3sGjRIi1evDjgNX/o9ddfV2xsrMaPH6+tW7cachrlgrq6\nOp05c0Zbt27VV199pXnz5ukvf/mLYfVHjRql1tZWpaam6ttvv9WWLVsCXnPy5Mk6depU+88//H2H\nh4fL6XQaWv9CcPnkk09UVFSkoqKiHu3P7GvVmzl/zJw7krnzh7nT87nDEUAgCEVERKipqan9ZyPf\nwCTpzJkzmjVrlu677z5NmTLFsLrS+TexAwcOKCMjQ5999pmysrJUW1trSO2YmBiNHz9eDodDQ4YM\nUb9+/VRXV2dIbUl69dVXNWrUKO3evVtvvfWWsrKy1Nraalh9SRe9zpqamhQVFWVofUnatWuXVq9e\nrVdeeUUxMTE92tbsuSOZN3/MnDuSufOHuXNeT+YOARAIQmZeb7u2tlaZmZlatmyZpk+fbljdC7Zv\n367CwkIVFhZq+PDheu655zRgwABDat90003661//KkmqqqpSS0tLjwNIb7S0tKh///6SpKioKLlc\nLrndbsPqS1JycrLKysokSaWlpRo9erSh9d966y0VFRWpsLBQAwcO7PH2Zl+r3sz5Y+bckcydP8yd\nns8dTgEDQWjSpEnav3+/Zs6cKen8h4uNsmXLFjmdThUUFKigoEDS+b+ue/pZrL7o9ttv19/+9jc9\n+OCDcrvdysvLk81mM6z+nDlztGLFCj3yyCM6d+6cli5dqrCwMENqXxhnVlaWVq5cKZfLpaSkJKWm\nphpW3+12a/369fr5z3+uBQsWSJLGjh2rhQsXer0fM+eOxPwxa/4wd3o+d7gWMAAAgMVwChgAAMBi\nCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQACW5nQ6NX/+fFVXV+s3v/mN2e0AfQZz\np28jAAKwtIaGBh05ckTx8fF65ZVXzG4H6DOYO30bXwQNwNLmzp2rDz/8ULfffrsqKiq0d+9eZWVl\nKTQ0VBUVFWpsbNSTTz6padOmmd0qEFSYO30bRwABWNrKlSsVHx+vFStWXHR7dXW1XnvtNf3xj3/U\n888/r9raWpM6BIITc6dvIwACsLSOToLYbDbNmDFDISEhuvrqqzVq1Cj94x//MKE7IHgxd/o2AiAA\ndMBut7f/2+12y+FwmNgN0Hcwd/oGAiAAS3M4HGpra7voaIbH49E777wjSTp9+rTKy8s1evRos1oE\nghJzp28jlgeBNWvW6Prrr9f9998f0DoLFizQqVOn1NzcrJqaGiUkJEiSnnnmGb3zzju6+eabA94D\nEGwGDBign/3sZ8rOzpbNZpN0/jRWc3Ozpk+fLpfLpbVr1yo6OtrkToHgwtzp2wiAQeDCxAm0/Px8\nSVJZWZlefvllFRYWtt/35z//2ZAegGDjcDhUXFx8ye1Tp07V3XffbUJHQN/A3OnbCIAmee6557R3\n714NGDBAoaGhuv766yVJL730kj766CN9++23iomJUX5+vvbt26ePPvpIL7zwgqTzQa5fv356/PHH\n2/f30ksv6YMPPrioxr333qvMzMxLanf2zT/vv/++/vSnP+ns2bOaO3euHnroIb388suy2WxasGCB\nJGnChAkqLCzUxx9/rDfeeEPffvutJkyYoMWLF/vl9wIAAAKPAGiC3bt369ChQ9q1a5caGxvbT7ue\nPHlSx48f186dOyVJy5cvV0lJiR5++GFt3rxZLS0tCgsLU0lJibZv337RPhcvXtyrEObxeNTa2qrX\nXntNX3zxhX7961/roYce6vDo5IXbqqur9e677yokhI+S4vKyYcMGs1sA+iTmTt9BADRBWVmZ7rzz\nTtntdkVHR+uOO+6Qx+PR4MGDtXz5cu3cuVPHjx/Xp59+qsGDBys8PFwpKSnavXu3Bg4cqMGDBysu\nLu6ifXZ0BHDq1KmaM2eOVz3ZbDbdcccdkqR/+7d/U319vaTzwfDHIfDCEcTrrruO8AcAQB9EADSB\nzWaT2+1u//nCEvlDhw5p6dKlyszMVGpqqux2e3vYeuCBB/T73/9egwYN0vTp0y/ZZ2+PAEr/t3T/\nh4HPZrNddMr43Llz7f8OCwvrVT0AAGAODt+Y4Fe/+pV27dql1tZWNTY2at++fZKkv//977r55pv1\n8MMPKykpSfv371dbW5skafTo0aqqqlJZWZkmTpxoWK8xMTH68ssvJUn/+te/VFNTY9iiFQAAEBgc\nATTBhAkTdOjQIU2dOlUxMTEaOnSobDab7rrrLi1cuFD33XefYmJilJKSotOnT7dvN2nSJDU0NCg0\nNLRX9W02W5ef7fvhv6dMmaL//d//1ZQpU/TLX/5S1113XYenhQEAQN9h83S2JFRSW1ubcnNzVVlZ\nKZvNpmeffVZXXHGFsrKyFBISomHDhikvL48wYIDW1lZlZmYqJydHycnJZrcDAAD6sC5PAe/bt08h\nISHasWOHnn76ab344ovauHGjlixZoqKiInk8Hu3Zs8eoXi2rurpa48eP14033kj4AwAAvdblEUDp\n/FFAu92uN954Qx9//LEOHDig0tJSSdKePXu0f/9+rVq1ypBmAQAA0HvdLgKx2+3KysrSunXrNHXq\n1ItWhIaHh8vpdAa0QQAAAPiXV4tANm7cqNraWs2YMUOtra3ttzc1NSkqKqrb7Vk0AAAAEDy6DIBv\nvvmmqqqq9MQTTygsLEwhISEaMWKEysrKNHbsWJWWlmrcuHHdFrHZbKqpMedIYVxcpGm1za7P2M0d\nOwAAwarlAFFOAAARlUlEQVTLAJiamqqsrCw9+uijOnfunHJycjR06FCtXLlSLpdLSUlJSk1NNapX\nAAAA+EGXATAsLEybN2++5PbCwsKANQQAAIDA4kogAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgA\nAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAA\nAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMU4urrT5XIpOztbX3/9tVpbWzVv3jz9\n9Kc/1RNPPKHExERJUlpamu6++24jegUAAIAfdBkAS0pKFBsbq02bNqmhoUHTpk3T/PnzlZmZqdmz\nZxvVIwAAAPyoywCYmpqqO++8U5LkdrvlcDh0+PBhHT9+XHv27FFCQoKys7PVv39/Q5oFAABA73X5\nGcDw8HD1799fjY2NWrRokRYvXqwbbrhBy5cv1/bt2zVo0CDl5+cb1SsAAAD8wObxeDxdPeDMmTNa\nsGCB0tPTNX36dDmdTkVGRkqSvvzyS61du1Z/+MMfjOgVAAAAftDlKeDa2lplZmYqLy9Pt9xyiyTp\nscceU05Ojm644QYdPHhQI0aM8KpQTY2z9936IC4u0rTaZtdn7OaOHQCAYNVlANyyZYucTqcKCgpU\nUFAgScrOztaGDRvkcDgUHx+vNWvWGNIoAAAA/KPbU8D+YuUjQYzdWrUv1AcAIFjxRdAAAAAWQwAE\nAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIg\nAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAW4zC7AX9ra2tTZeWx9p/r\n6yNUV9d4yeMSE4fKbrcb2RoAAEBQuOwCYGXlMS3a9LbCo+M7fUxzQ7V+t+xeJSUNM7AzAACA4HDZ\nBUBJCo+OV0TMNWa3AQAAEJS6DIAul0vZ2dn6+uuv1draqnnz5ikpKUlZWVkKCQnRsGHDlJeXJ5vN\nZlS/AAAA6KUuA2BJSYliY2O1adMmNTQ0aNq0aUpOTtaSJUs0ZswY5eXlac+ePZo4caJR/QIAAKCX\nulwFnJqaqqeeekqS5Ha75XA4VFFRoTFjxkiSUlJSdODAgcB3CQAAAL/pMgCGh4erf//+amxs1KJF\ni/T000/L7XZfdL/T6Qx4kwAAAPCfbheBnDlzRgsWLFB6erruuecebdq0qf2+pqYmRUVFeVUoLi7S\n9y57oL4+wqvHxcZGGNaTUXWCrbbZ9c0eOwAAwarLAFhbW6vMzEzl5eXplltukSQlJyerrKxMY8eO\nVWlpqcaNG+dVoZoaY44UdvSdf509zoie4uIiDRt7MNU2u34wjB0AgGDVZQDcsmWLnE6nCgoKVFBQ\nIEnKycnRunXr5HK5lJSUpNTUVEMaBQAAgH90GQBzc3OVm5t7ye2FhYUBawgAAACBxbWAAQAALIYA\nCAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEE\nQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIvx\nKgCWl5crIyNDklRRUaGUlBRlZGQoIyNDu3btCmiDAAAA8C9Hdw/Ytm2b3n77bfXv31+SdPjwYc2e\nPVuzZ88OeHMAAADwv26PACYkJCg/P18ej0eSdOjQIb3//vt69NFHlZOTo6ampoA3CQAAAP/pNgBO\nnjxZdru9/eeRI0dq+fLl2r59uwYNGqT8/PyANggAAAD/6vYU8I9NmjRJkZGRkqSJEydq7dq1Xm0X\nFxfZ01I+qa+P8OpxsbERhvVkVJ1gq212fbPHDgBAsOpxAHzssceUk5OjG264QQcPHtSIESO82q6m\nxtnj5nxRV9fo9eOM6CkuLtKwsQdTbbPrB8PYAQAIVl4HQJvNJkl69tln9eyzz8rhcCg+Pl5r1qwJ\nWHMAAADwP68C4MCBA1VcXCxJGj58uHbs2BHQpgAAABA4fBE0AACAxRAAAQAALIYACAAAYDEEQAAA\nAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIA\nAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxTjMbqCn/rx7j6pq6jq9v+qb05KuMq4hAACAPqbP\nBcDST46pxpbU6f01J77SlVEGNgQAANDHeHUKuLy8XBkZGZKkEydOKC0tTenp6Vq9erU8Hk9AGwQA\nAIB/dRsAt23bptzcXLlcLknShg0btGTJEhUVFcnj8WjPnj0BbxIAAAD+020ATEhIUH5+fvuRvoqK\nCo0ZM0aSlJKSogMHDgS2QwAAAPhVtwFw8uTJstvt7T//8JRveHi4nE5nYDoDAABAQPR4EUhIyP9l\nxqamJkVFebfiIi4usqelOtTvCofk6v1+YmMj/NZTd4yqE2y1za5v9tgBAAhWPQ6AycnJKisr09ix\nY1VaWqpx48Z5tV1NjX+OFH7fek6y9X4/dXWNfuupK3FxkYbUCbbaZtcPhrEDABCsvA6ANtv51JWV\nlaWVK1fK5XIpKSlJqampAWsOAAAA/udVABw4cKCKi4slSYmJiSosLAxoUwAAAAgcLgUHAABgMQRA\nAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALCYHn8RtBW0tbWpsvJYt49LTBx60WXyAAAA+gIC\nYAcqK49p0aa3FR4d3+ljmhuq9btl9yopaZiBnQEAAPQeAbAT4dHxioi5xuw2AAAA/I7PAAIAAFgM\nARAAAMBiCIAAAAAWY8nPAHrcbp08eaLT+7u6z1sXVhLX10eorq6x08exkhgAABjNkgGwxVmjF3bW\nKjz6TIf3nz11RFcNTO5VDVYSAwCAYGXJACh1vcq3uaEq4DUAAADMwmcAAQAALIYACAAAYDEEQAAA\nAIshAAIAAFgMARAAAMBifF4FfP/99ysiIkKSNGjQIK1fv95vTQEAACBwfAqA33//vSSpsLDQr80A\nAAAg8Hw6BfzZZ5+ppaVFc+bM0axZs1ReXu7vvgAAABAgPh0BvPLKKzVnzhzNmDFDlZWVevzxx7V7\n926FhHSeJ+PiIn1u8of6XeGQXH7ZVa/FxkZ0Oq76+ohe78MfArnvYK9v9tgBAAhWPgXAxMREJSQk\ntP/7Jz/5iWpqanT11Vd3uk1NjdO3Dn/k+9Zzks0vu+q1urrGTsfV1fV/vd1Hb8XFRQZs38FePxjG\nDgBAsPLpFPDrr7+ujRs3SpKqqqrU2NiouLg4vzYGAACAwPDpCOCDDz6oFStWKD09XZK0YcOGLk//\nAgAAIHj4FAAdDoc2bdrk714AAABgAJ+/BxC953G7dfLkiW4fl5g4VHa73YCOAACAFRAATdTirNEL\nO2sVHn2m08c0N1Trd8vuVVLSMAM7AwAAlzMCoMnCo+MVEXON2W0AAAALYeUGAACAxRAAAQAALIYA\nCAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEE\nQAAAAIshAAIAAFiMw+wG+iqP262TJ090en9X9/mzTltbmySb7PZLs3x9fYTq6hq7fMwFiYlDZbfb\n/dAxAAAIdgRAH7U4a/TCzlqFR5/p8P6zp47oqoHJhtS5MvIqhUfHd7qP7h7T3FCt3y27V0lJw3rd\nLwAACH4EwF4Ij45XRMw1Hd7X3FBlWJ2u7vf2MQAAwDp8CoBut1urV6/W0aNHFRoaqnXr1mnw4MH+\n7g0AAAAB4NMikPfee08ul0vFxcV65plntHHjRn/3BQAAgADxKQB+8sknuvXWWyVJI0eO1KFDh/za\nFAAAAALHp1PAjY2NioiIaP/ZbrfL7XYrJCTw3yoT4nLK3lLR6f32lq/U7PF0uY8WZ50km8/3G7UP\no+o0N1R3uT0AALi8+BQAIyIi1NTU1P6zN+EvLi7Sl1KXePXlPL/sB8bw1/Pe12oDABDMfDpkN2rU\nKJWWlkqSPv30U1177bV+bQoAAACBY/N4ujlf2gGPx6PVq1fr888/lyRt2LBBQ4YM8XtzAAAA8D+f\nAiAAAAD6Lq4FDAAAYDEEQAAAAIshAAIAAFhMwAKg2+3WqlWrNHPmTGVkZOjkyZOBKtUhl8ulZcuW\nKT09XTNmzNDevXsNrS9JZ8+e1W233abjx48bXnvr1q2aOXOmHnjgAb3xxhuG1na73VqxYoXS0tKU\nnp6uY8eOGVK3vLxcGRkZkqQTJ06011+9erUC/VHXH9Y+cuSI0tPTlZGRoTlz5ujs2bMBrQ0AQE8F\nLACafbm4kpISxcbGqqioSK+++qp++9vfGlrf5XJp1apVuvLKKw2tK0kff/yx/vnPf6q4uFiFhYX6\n6quvDK3/4YcfqqWlRTt27ND8+fO1efPmgNfctm2bcnNz5XK5JJ1fmb5kyRIVFRXJ4/Foz549htVe\nv369Vq5cqcLCQk2ePFnbtm0LWG0AAHwRsABo9uXiUlNT9dRTT0k6f0TKbrcbWv/5559XWlqa4uLi\nDK0rSfv379e1116rJ598UnPnztWECRMMrR8WFian0ymPxyOn06nQ0NCA10xISFB+fn77kb6KigqN\nGTNGkpSSkqIDBw4YVvvFF1/U8OHDJUnnzp1Tv379AlYbAABf+HQlEG+Yebk4SQoPD2/vY9GiRVq8\neLEhdSXp9ddfV2xsrMaPH6+tW7cG/PTjj9XV1enMmTPaunWrvvrqK82bN09/+ctfDKs/atQotba2\nKjU1Vd9++622bNkS8JqTJ0/WqVOn2n/+4e88PDxcTqfTsNoXQv8nn3yioqIiFRUVBaw2AAC+CFga\n8+Vycf525swZzZo1S/fdd5+mTJliWN3XX39dBw4cUEZGhj777DNlZWWptrbWsPoxMTEaP368HA6H\nhgwZon79+qmurs6w+q+++qpGjRql3bt366233lJWVpZaW1sNqy/potdaU1OToqKiDK2/a9curV69\nWq+88opiYmIMrQ0AQHcClsjMvlxcbW2tMjMztWzZMk2fPt3Q2tu3b1dhYaEKCws1fPhwPffccxow\nYIBh9W+66Sb99a9/lSRVVVWppaXF0BDS0tKi/v37S5KioqLkcrnkdrsNqy9JycnJKisrkySVlpZq\n9OjRhtV+6623VFRUpMLCQg0cONCwugAAeCtgp4AnTZqk/fv3a+bMmZLOfyjfSFu2bJHT6VRBQYEK\nCgoknT8yZYXPY91+++3629/+pgcffFBut1t5eXmy2WyG1Z8zZ45WrFihRx55ROfOndPSpUsVFhZm\nSO0L48zKytLKlSvlcrmUlJSk1NRUQ2q73W6tX79eP//5z7VgwQJJ0tixY7Vw4cKA1wcAwFtcCg4A\nAMBi+CJoAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAAvE06nU/Pnz1d1dbV+\n85vfmN0OAAAIYgTAy0RDQ4OOHDmi+Ph4vfLKK2a3AwAAghhfBH2ZmDt3rj788EPdfvvtqqio0N69\ne5WVlaXQ0FBVVFSosbFRTz75pKZNm2Z2qwAAwGQcAbxMrFy5UvHx8VqxYsVFt1dXV+u1117TH//4\nRz3//POqra01qUMAABAsCICXiY4O5NpsNs2YMUMhISG6+uqrNWrUKP3jH/8woTsAABBMCICXObvd\n3v5vt9sth8NhYjcAACAYEAAvEw6HQ21tbRcdCfR4PHrnnXckSadPn1Z5eblGjx5tVosAACBIcDjo\nMjFgwAD97Gc/U3Z2tmw2m6Tzp4Cbm5s1ffp0uVwurV27VtHR0SZ3CgAAzEYAvEw4HA4VFxdfcvvU\nqVN19913m9ARAAAIVpwCBgAAsBi+BxAAAMBiOAIIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAA\nABZDAAQAALCY/w8zVcLsg+N+dQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114261d90>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame({'name': ['yes', 'yes', 'no', 'maybe'], 'values': [0, 5, 1, 2]})\n",
      "g = df.groupby('name')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "4"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list(g.values)[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "('maybe', 3    2\n",
        " Name: values, dtype: int64)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "[('maybe',     name  values\n",
        "  3  maybe       2\n",
        "  \n",
        "  [1 rows x 2 columns]), ('no',   name  values\n",
        "  2   no       1\n",
        "  \n",
        "  [1 rows x 2 columns]), ('yes',   name  values\n",
        "  0  yes       0\n",
        "  1  yes       5\n",
        "  \n",
        "  [2 rows x 2 columns])]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "type(list(g)[0][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "str"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}

nbformat4.ipynb