{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ploting and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A brief matplotlib API primer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10f6fbc88>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kil7KSSSr5kzlNHEmaOZczlROE2cqq8zWx1rgPzLzvzPzOeDvgT9Z6MDdu+Gtb4WbbupV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\nAKqV0Q7eLAWuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f6216a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figures and Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f749f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax1 = fig.add_subplot(2, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax2 = fig.add_subplot(2, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax3 = fig.add_subplot(2, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from numpy.random import randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10f9071d0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XkZCQcMNrnnhrykhr1qzBK6+8UuNx69atw8CBNT7iIR/0yCOPVFqG29uxCJhB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\nGSZ2IiIvw8RORORlmNiJiLzM/wPpbl4fZay0lgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8b85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(randn(50).cumsum(), 'k--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_ = ax1.hist(randn(100), bins = 20, color = 'k', alpha = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10f907fd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax2.scatter(np.arange(30), np.arange(30) + 3*randn(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B8OAvTnrAqHfMtU7mqiHH/80px//VyfF/dXL8nyRpIpu8JFXMJi9JFbPJS1LFbPKSVDGb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\nzCYvSRWzyUtSxf4fYj+zmovhQvMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f90d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10f9f1b38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10fa3e860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10fa7b828>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10fac4860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10fad4390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10fb55160>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fa3e860>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes[0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adjusting the spacing around subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc73470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axes[i, j].hist(randn(500), bins = 50, color = 'k', alpha = 0.5)\n",
    "plt.subplots_adjust(wspace = 0, hspace = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colors, Markers, and Line Styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1101b5518>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yzVMACA8OxzsPvIOs/CxtGd5c+ybua3ofBrccrC0DEVmPEcsUTwFoctO/G5c8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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc797f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(randn(30).cumsum(), 'g--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1102a12e8>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rn9mduxvHzx3HAy0f0B2FPISFnciCyh3lKCwpvO57c3bOwZMJTyIwINDkVGQW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\nZDEs7EREFsPCTkRkMf8PNi8EewyxkzoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101be828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(randn(30).cumsum(), linestyle = '--', color = 'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1103949b0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tRKFsxowZRofQJJp0norIKBHZJSJ7RORJLcoMRLt27fxK3E1x991346OPPvL5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\nTOxERDbzv2NoEY7btrFlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102bbf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(randn(30).cumsum(), 'ko--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1104940f0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ejz/+uOY4fFl5331CQgLy8vIQERFh6GIf5L8485Q06d+/P+bMmePxGPZyGzZs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\nWKymgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11036e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(randn(30).cumsum(), 'ko--', linestyle = 'dashed', marker = 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = randn(30).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110586c88>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ERAbRNRSTCiBWRJqJSG0AQwAsM6BdIiLygNdDMUqpUhEZA2A1yv+jmKOU+snrZEQBxpOH\nlRNdD5cUIPIBZWVlaNmyJTZv3ozo6GjdcciHcEkBIj8VFBSELl26cA13MgQLO5GPeOKJJ/Dll1/q\njkE2wKEYIh9x9uxZNG7cGIcOHUJERITuOOQjOBRD5MfCwsLQp08frFixQncU8nMs7EQ+ZMiQIcjK\nytIdo0by8vKQkZGhOwZdB4diiMhtSiksW7YMs2fPxrfffqs7jq1xKIaITHf69GnExMSgW7du2LJl\nC0pKSnRHoquwsBORWzZv3ozY2Fg0atQIzZs3R1pamu5IdBUWdiJyi9PpRM+ePQEAPXv2xIYNGzQn\noquxsBORWzZs2MDC7uNY2Il80P79+/HPf/5Td4xrnDlzBhkZGejSpQuA8sLerl07zanoapwVQ+SD\nsrOz0b59e/z6668+9TCS1NRUTJ48mTNhLOTJrBgWdiIf1alTJ7z55pvo06eP7ihXUEoFzHNqfQGn\nOxLZyKBBg3xy7RgWdd/HM3YiH7Vv3z44HA4cPnyYz0YNYDxjJ7KRVq1aISIiAlu3btUdhfwMz9iJ\nfFhGRgaaN2+OW265RXeUG1q2bBlKS0vx+OOP645iOzxjJ7KZ+Ph4nynqTqcTFy5cuO7PioqKsHDh\nQosTUVV4xk5E1Tp//jwaNWqEX3/9FeHh4df8PDs7Gx06dEBeXh4vrhqMZ+xEZIqtW7ciPj7+ukUd\nAGJiYhAeHo49e/ZYmuvo0aM4ceKEpX36AxZ2IqrW5csIVEXH8gIdO3ZEfHy8pX36AxZ2Ij9w7tw5\nFBUVaet/w4YNcDgcN9xGR2EvLi5G8+bNwWHeK3GMncgP/PnPf0bjxo3x4osvWt73hQsX0LBhQxw7\nduyGF3JPnDiBY8eOIS4uzpJcBQUFaNGiBU6ePIng4GBL+tTB8jF2EXlSRHaJSKmItPemLSKqWkJC\ngra7UE+fPo2JEydWOzsnIiLCsqIOANu2bUPHjh1tXdQ95dUZu4jcBaAMwIcA/qKUqnLFfZ6xE3nO\n5XIhOjoaaWlpiImJ0R3HJ2RkZCArKwuPPPKI7iimsvyMXSm1VymVCYDzm4hMFBISgkcffRRLly7V\nHcVnxMfH276oe4oXT4n8xKBBg7BkyRLdMXwaRwXKVbvQs4isARB1+VsAFICXlVLL3eksKSmp8rXD\n4aj2KjsR/b/f/OY3+OKLL1BaWurT48pKKZSVlVmecfbs2SgsLNRygdlITqcTTqfTqzYMmRUjIt8B\nGM8xdiIaPXo0OnXqhOHDh1va748//oiBAwfiwIEDPvVwEm/pvvOU4+xENvPXv/4Vv/zyi1ufiY+P\n1/Ic1DZt2qBJkyZYtWqV5X37Gm+nOyaISA6ALgBWiMg3xsQiIt1KSkrw97//HREREW59zooblcaO\nHYusrKxr3h81ahQ++OADU/v2B7xBiYiua8uWLRg9ejR27Njh1ueUUoiKisIPP/xgytTM4uJiRERE\nIC8vD2FhYVf87Pz582jatClSU1PRokULw/vWQfdQDBHZSE3Wh7keEUGPHj1MO2tPT09Hy5Ytrynq\nAFCnTh2MGTMGu3btMqVvf8HCTuRnDh06hFdffdX0fpxOp0eFHQB69+7t9th8TaWkpKBz585V/jwp\nKQkDBw40pW9/waEYIj9z7NgxtG7dGsePHzetD5fLhYiICBw4cAANGzZ0+/NKKdPWZf/973+P3r17\n4w9/+IMp7fsaDsUQBYDo6Gi4XC7k5eWZ1oeI4LvvvvOoqF/6vFlSUlJw//33m9a+HbCwE/kZEUFc\nXJypD7UIDg5Ghw4dTGvfG4sXL8Y999yjO4ZPY2En8kP33nsvdu/erTuGFu3atfPpO299AQs7kR+K\ni4sL2MJeUyUlJRgwYADOnTunO4rlWNiJ/FBCQgJGjBihO0a1Nm7ciFOnTmnpu1atWggODsa//vUv\nLf3rxMJO5IeaNWuGjh07mtJ2SUmJYW1Nnz4da9euNaw9dwXqnags7ER0hUGDBmHFihWGtGXk8gJl\nZWVuf6Z///7Iy8vD9u3bDcngL1jYiahSaWkpNm7caNhvA0YW9uTkZIwcOdKtzwQHB+PZZ58NuLN2\nFnYiqrRz505ERUUhOjrakPbat2+PrKwsFBQUeN1WSkoKWrVq5fbnRowYgW3btqG0tNTrDP6ChZ2I\nKjmdTkMfgBMSEoKuXbti48aNXrfl6Y1J0dHRSE9PD6gpkizsRH5q165d+OMf/2hom54u/HUjo0aN\nQmRkpFdtFBcXY9euXR7fNGXmnbC+yD6PGSEKMBEREVi2bJmhbebm5hpe2BMSErxuIz09Ha1atbru\nio50LRZ2Ij/VuHFjXLx4EcePH0ejRo0MaXPLli2GtGO0ffv2oVu3brpj+A2u7kjkx7p374433njD\n8LNsX2TmipG+jKs7EgWYQFozxoiivmPHDkyYMMGANL6NhZ3Ij5m9yqPRPLnJyEixsbGYN28e9u3b\npzWH2TgUQ+THCgoK4HK5EBUVpTtKtTIzM/HUU09h27ZtCArSd0752muv4ZdffsG8efO0ZXAHh2KI\nAkxkZKQhRf3QoUPIyMgwIFHVYmNjoZTCypUrTe2nOmPHjsWKFStMe3SfL2BhJyIsXLgQ8+fPN7UP\nEcGECRPw1ltvufW59PR0XLhwwbAc9evXx3PPPYdp06YZ1qavYWEnIqSkpKBLly6m9/Pkk08iJycH\nW7durdH2Sin07t0bJ0+eNDTHuHHjkJWVBZfLZWi7vsKrMXYRmQFgIIBiAL8AGK6UKqpiW46xE/kg\npRSioqKQlpaG22+/3fT+3nvvPTidTixZsqTabTMzM9GnTx9kZ2ebnstX6RhjXw0gTinVFkAmgBe9\nbI+IPODNSdPBgwdRq1YtS4o6UL4oV2RkZI0W5eKDqz3jVWFXSq1VSl2av7QVgDV/M4io0ubNmzFg\nwACPP2/VMMwlYWFh+Oijj2q0KBcLu2eMHGMfAeAbA9sjohpo1qwZ0tLSPP58w4YNMXToUAMTGYeF\n3TPVrhUjImsAXD6fSgAoAC8rpZZXbPMygBKlVPKN2kpKSqp87XA4DF0elChQNWnSBMXFxcjPz0fD\nhg3d/nzfvn1NSOU9pRTuuusutG/f3vS+XC4XQkJ8Y+ksp9MJp9PpVRte36AkIsMAjATQWylVfIPt\nePGUyCTdunXDm2++iR49euiO4neKi4sRFxeHTZs2GfaAESNZfvFURPoDmADg0RsVdSIylz+vGXPu\n3Dmt/YeGhuLhhx/G3/72N605jOTtGPt7AMIBrBGRNBGZbUAmInJTXFwcMjMzdcdw2/79+xEfH699\nPvnEiRMxZ84cHD9+XGsOo3CtGCIbKCkpQUhIiF8ua9uzZ0+MGjUKTz31lNYco0aNQkREBN544w2t\nOa7GtWKIAlStWrU8KuqTJk3C6dOnTUhUc5eWGdB94jdp0iR8+OGHOHHihNYcRmBhJwpQeXl5+PDD\nD7U/bm7AgAEoLi7GunXrKt9LT083/LF/1WnevDmSkpJw6tQpS/s1Aws7UYBKSUlB586dtS6hCwBB\nQUH4y1/+csXiYEuXLq3xejJGGjt2LO644w7L+zUaCztRgNq6davP3PyTmJiITp06VS4zwBuTvMPC\nTmQTLpfLrVkdVi8lcCOhoaGYOnUqgoODoZTCtm3b0LlzZ92x/BYLO5FNrFu3DkOGDKnRtqWlpT5b\nPDMzMxEeHo7GjRvrjoLCwkLdETziG/fQEpHX3LlJqaysDJ9//rlHSxCYzVeGYYqLi9GsWTOEh4ej\ndevWiI+PR+vWrStf+zLOYyeyCaUU6tevjwMHDiAyMlJ3HI9lZGSgsLAQDzzwgO4oUEohKysLO3fu\nxM6dO5GRkYG8vDx89913lmXwZB47CzuRjXTt2hUzZszAgw8+qDsKGYQ3KBEFOH9eM8afffvtt9iz\nZ4/uGJVY2IlspHPnztoX1QpE69evx+LFi3XHqMShGCIiL61cuRJvv/32FXfPGoVj7ERUrVmzZiEo\nKAhjx47VHcU2Tp06haZNm6KgoAC1a9c2tG2OsRNRtb777jtERUVVvyHVWP369XHnnXd69YhCI7Gw\nEwUQpZTPzBO3mx49euD777/XHQMAb1AiCijZ2dkAgJiYGM1J7Gf48OE4e/as7hgAWNiJbCc/Px9H\njx5F69atr/nZ1q1b0aVLF798IIeva9eune4IlTgUQ2QzP/zwA55//vnr/mz79u0chgkAnBVDZDPZ\n2dm4//77cezYsWt+VlpaiosXL6JOnToakpEnOCuGiNC0aVOcPXv2uo94Cw4OZlEPACzsRDYjIrjn\nnnt86hZ3shYLO5ENxcXFcc0YTYYOHYrMzEytGVjYiWyoX79+qF+/vu4YAWvDhg1a+/eqsIvIqyLy\no4iki8haEbndqGBE5LnBgwdj8ODBV7yXk5MDTmAwX48ePbBx40atGbyaFSMi4UqpMxWvxwJoo5T6\nYxXbclYMkSYFBQW44447cOLECQQHB+uOY2s//fQTBgwYgIMHDxrSnuWzYi4V9QphAPK9aY+IzJGS\nkoJOnTqxqFvg7rvvxpkzZ5CTk6Mtg9dj7CIyVUSyAQwDMM3rRERkuEt3nJL5RET7cEy1SwqIyBoA\nly8FJwAUgJeVUsuVUpMBTBaRiQD+DmB4VW0lJSVVvnY4HHA4HJ6lJiK3pKSkYMyYMbpjBIyPP/4Y\n9erV8+izTqcTTqfTq/4Nu/NURJoCWKWUuu7juznGTmStffv24eDBg+jbty8iIyOxd+9e3Hrrrbpj\nkZssH2MXkdjLvk0AkO5Ne0RknF9++QUzZ85EQUEBevTowaIeQLydFfNvAK0AlAI4AOB/lVJ5VWzL\nM3YiC2VlZaFr1644evSo7ijkBT4aj4gqKaVQt25dZGdno0GDBrrjkIe4CBgRVeKaMfpdb4VNK7Cw\nE9kY14zR5+LFi2jZsiWKioos75uFncjGEhMTcdddd+mOEZBq166Njh074r///a/lfbOwE9lY3759\n0bNnT90xApauB1yzsBMRmUTXHaicFUNEZJIzZ84gKioK+fn5Hj+5irNiiIh8SHh4OAYNGmT5vQQ8\nYyci8mE8YyciIhZ2IiK7YWEnIrIZFnYiIpthYSciMllubi6Sk5Mt64+zYoiITHbs2DHExcUhPz8f\nQUHunU9zVgwRkQ9q3LgxIiMjLVuQjYWdiMgCVi4vwMJORGSBBx980LIFwVjYiYgscGmlRyuuNbKw\nExFZoEWLFpgyZQpcLpfpfXFWDBGRD+OsGCIiYmEnIrIbQwq7iIwXkTIRiTCiPSIi8pzXhV1EbgfQ\nF0CW93H8l9Pp1B3BVHbePzvvG8D9C0RGnLG/A2CCAe34Nbv/5bLz/tl53wDuXyDyqrCLyKMAcpRS\nGQblISIiL4VUt4GIrAEQdflbABSAyQBeQvkwzOU/IyIijTyexy4i9wFYC+Acygv67QCOAOislMq7\nzvacxE5E5AF357EbdoOSiBwE0F4pddKQBomIyCNGzmNX4FAMEZF2li0pQERE1jD9zlMR6S8iP4vI\nPhGZaHZ/VhORQyLyo4jsEJFtuvN4S0TmiEiuiOy87L0GIrJaRPaKyLciUk9nRm9UsX9TROSwiKRV\nfPXXmdEbInK7iKwXkd0ikiEif6543++P4XX2bWzF+7Y4fiISKiIpFbVkt4i8UfG+28fO1DN2EQkC\nsA9AHwBHAaQCGKKU+tm0Ti0mIgcAdLDLtQUReQDAGQDzlVKtK96bDqBAKTWj4j/nBkqpSTpzeqqK\n/ZsC4LRS6m2t4QwgItEAopVS6SISDmA7gMcADIefH8Mb7Ntg2Of43ayUOiciwQA2AxgP4FG4eezM\nPmPvDCBTKZWllCoB8C+UHwg7EdhozR2l1CYAV/8n9RiATytefwogwdJQBqpi/wCbXB9SSv2qlEqv\neH0GwE8on7Hm98ewin1rUvFjuxy/cxUvQ1FeV07Cg2NndkFqAiDnsu8P4/8PhF0oAGtEJFVERuoO\nY5JblVK5QPk/LgC3as5jhjEiki4i//THYYrrEZHmANoC2Aogyk7H8LJ9S6l4yxbHT0SCRGQHgF8B\nOJVSe+DBsbPNmaZG3ZVS7QEMAPBcxa/6dme3K+6zAdyhlGqL8n9QdviVPhzAvwGMqzi7vfqY+e0x\nvM6+2eaiGwQCAAABhklEQVT4KaXKlFLtUP5b1oMi4oAHx87swn4EQMxl31+6ick2lFLHKv48DmAp\nyoef7CZXRKKAynHOa25A82dKqeOXPQXmYwCddObxloiEoLzwLVBKfV3xti2O4fX2zW7HDwCUUkUA\nVgHoCA+OndmFPRVArIg0E5HaAIYAWGZyn5YRkZsrzh4gImEAHgKwS28qQwiuHLNcBmBYxetnAHx9\n9Qf8zBX7V/GP5ZIn4P/H8BMAe5RSsy57zy7H8Jp9s8vxE5GGl4aRRKQOypdr2QEPjp3p89grph7N\nQvl/InOUUm+a2qGFRKQFys/SFcrX3fnM3/dPRJIBOABEAsgFMAXAVwAWA2iK8uWZ/0cpdUpXRm9U\nsX+9UD5eWwbgEIA/XRrT9Dci0h3A9wAyUP73UqF8TadtABbBj4/hDfYtETY4fiISj/KLo5cmZCxQ\nSs2seM6FW8eONygREdkML54SEdkMCzsRkc2wsBMR2QwLOxGRzbCwExHZDAs7EZHNsLATEdkMCzsR\nkc38HwF328xkQhK+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11054c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data, 'k--', label = 'Default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110687940>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f+/hs3ympkPSjkk5IOizpXyT9o6TXaL48829GxP/21ccmdhjfL2terz0t6duSfn+rpjk2\ntq+W9ICkY5r/Xobmazp9WdI/aMT7cJex/ZYS7D/bb9D84OjWCRl3RMSfVZ9zUWvfcYESACTDwVMA\nSIZgB4BkCHYASIZgB4BkCHYASIZgB4BkCHYASIZgB4BkfggDvbUUGUTZzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110562c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data, 'k-', drawstyle = 'steps-post', label = 'steps-post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSWrMyEtSY/8LkDqzw0fZnlYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11081cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.legend(loc = 'best', label = '...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ticks, Labels and Legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seting the title, axis labels, ticks, and ticklabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSWrMyEtSY/8LkDqzw0fZnlYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1106939e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1109fd1d0>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(randn(1000).cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ticks = ax.set_xticks([0, 250, 500, 750, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],\n",
    "                            rotation = 30, fontsize = 'small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11093c4e0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.set_title('My first matplotlib plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1108cfa90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.set_xlabel('Stages')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11092d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110abfa90>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(randn(1000).cumsum(), 'k', label = 'one')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110abfac8>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(randn(1000).cumsum(), 'k--', label = 'two')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110827cc0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(randn(1000).cumsum(), 'k.', label = 'two')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x110ac42e8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anotations and Driving on a subplot"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ax.text(x, y, 'hello world!',\n",
    "        family = 'monospace', fontsize = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111b1e8d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X6tSpA8C+ffuoXLkycXFxjB49uliuy8/Pj9jYWHbu3MmKFSusN+4ff/yRVatW\nER4eTnh4OLt377be2mfPnk1oaCjt2rXj0KFDVnrp0qXp169fru1MmTKFLVu20L17dxYtWkSvXr1y\nLffTTz/x2GOPERYWxh133MHZs2c5f/48p06d4p577iEkJISnnnqKHTt2WOd069bNmnEGEBERQY0a\nNVBKERoaSnx8POXKlcPX15cHHniA//3vf3n6hwYNGkRsbCyxsbHWepi4uDg6depEixYtWLRoEX/9\n9Vee99PVdjSa4kL7IFxAROjXrx9RUVGMGTOm2Os/fvw4QUFBlC5t/DtsCuLMmTMAfPHFFw7l16xZ\nQ7NmzVyqu3HjxiQlJQFGDCgwVmLbeOedd7jvvvuIj4+nbNmyfPnllwwYMCDfYayi0K5dO06cOMGJ\nEycQESZOnMiDDz7oUCY6OprVq1cTExODt7c3UVFR1r3w8fHJ1y9Tv359Hn74YR544AGqVKliKVt7\nRISYmBg8PT0d0seMGUOXLl348ssvSUhIICoqyspzDlViv+q9VKlSZGZmUqpUKTZt2sTPP//MF198\nwdy5c/n5559dui8jRozgm2++oXnz5nz88cfWqvjcuJx2NJqioC0IF9izZw+enp4sXLjQre3YFs/Z\nOsWlS5cC8OeffzJ79mzAmJ0UGRnpssPUx8eHunXrAlgxoVavXk2LFi0AY4pr48aN2blzJ2XLluWu\nu+6idevWxTL7R+zG3Hft2kV2djaVKlWiR48efPjhh9aMrSNHjnD8+HFSU1OpUKEC3t7e7Nq1y8HX\nYF+XM99//731fc+ePZQuXZry5csTEBDgsIlS9+7deeutt6zjbdu2Acaq9lq1agHw0UcfFfo6bVZI\nz549efPNN/nzzz9dPvfs2bNUr16djIwMPvvsM7e1o9EUhRtOQaxevZqhQ4cWajbN8uXLGTlypMO+\nC+7E5lS2dWAATZo0AQyLoLCUL18eMGYpgTEbq3Xr1gwYMIAKFSrQokULoqOjrXJNmjSxrI3LIS0t\nzZrWOXjwYBYuXIhSim7dunHvvfdy880306JFC/r378/Zs2fp2bMnGRkZBAcHM2nSJIfZR/lZD598\n8glNmzYlPDyc4cOHs2jRIpRS3H777fzvf/+znNRz5szh999/p2XLljRv3pz33nsPgHHjxjFhwgRa\ntWpVqNAkNplOnz5Nnz59aNmyJZ06dSrUyviXXnqJiIgIOnbsaE1ucEc7Gk2RyMs5ca19KMBJffr0\naTl48KAN5dWCAAAgAElEQVQAcvvtt0uLFi3kwoULLjlxunTpIl9//bVLZYvC9u3bBbA+ISEhcvr0\naalQoYJUrlxZAMtRPXz48ELX36tXL6vutLQ0mTNnjowZM8bK//777wWQW2+9VUREpk6dKpMnTy6u\ny9NoNFcx3MhO6szMTDp27EjZsmV54IEH6NixI8uWLaNy5co8++yz+Z57/vx5hg0bxurVq+nYsaPb\nZAwODraC7y1atIgzZ87w/PPPk5KSYr3VN2zYkCVLlhRpfnxERIT1fe/evTkW2oWGhgJYw1h169Zl\n2bJlRb4ejUZzfXBdK4gvvviC7du389dff/HQQw/xww8/UK9ePTw9PXn44Yf5559/cj3PNswwaNAg\na1imQoUKbpXV1mH37t2bmjVrMnv2bObMmeMwtDJgwIAiLfrq0aMHAL169WLo0KGsWLHCQUFUr16d\njz76yHJ8d+3alZ07d17O5Wg0muuA61JBnDlzhqysLAYMGMB3333nMPuobNmygDEbxXkBWXp6Ol26\ndKFUqVJs376dX375hZiYGCZNmuR2mX19falQoQLlypWzFt499thj+TpnXaVNmzaMGTOGDRs2sG3b\nNtasWeMwQ0kpxYgRIyxlVLVq1ULvUQFw7Ngxxo0bxzPPPMMzzzzD008/rUNEaDTXMNfdNNcnn3zS\nYabKhg0bCAsLo0WLFixZssRy8uamIA4fPsyaNWto0KABP//8M0FBQURERDgM0biL+vXrM27cOABr\nGqZSiokTJ1qhI4pK6dKlmTt3Lt26daNv374A+c6h9/b25uLFiwWGl9i3bx89evSwVn4fPXqUrKws\nhzKzZs1ycO4PHz6cl19+Oc86RYS///6bm266Kcd0VI1Gc2VxmwWhlJqvlDqmlMoxF08p9YxSKlsp\nVdEubaJSaq9SaqdSqrtderhS6k+l1B6l1OyC2l25cqU1OwWMKZC2oZMBAwYQFhYG5K4gTp8+TcuW\nLalTpw5PPvkk9erVK/R1F5WyZctaYbftO8b7778/z1hKhaV7d+u24u/vn2c5pRR+fn6kpKRYcZpy\no3bt2pw8eZJDhw5x6NChHMrBhi3/xIkTBAYG5ivjG2+8QaNGjfjuu+8KuBqNRuNu3DnE9BHQwzlR\nKVUb6AYk2KUFAQOAIKAX8I66NPj+X2CUiDQBmiilctRp47333iMhIYERI0aQnJzM559/Tu/evbnl\nlltylM1NQaSmplK2bFnLNzFz5sxCXXBx4eoq6cJiP6xkc37nxcWLF6lSpQp+fn78/fffedY3depU\nl/e9rlSpEoMHD86RnpmZyT333MPKlSv57bffqFGjhhUZV6PRlBxuUxAisg7IuZwVZgHjnNLuBBaL\nSKaIxAN7gQilVHUgQEQ2m+UWAn3zavORRx6hYcOGeHl5UbFiRQYNGsTy5cuthWL2eHl55akgfv75\nZw4dOpRjM54rhW1FdXGjlLJW3hY03z8jI8P6/thjj+VZ7pFHHnFpKMjf35/p06fnem2dOnVi2bJl\nfPvttxw7doywsDCH1d4ajaZkuKJOaqXUHcBBEYlzyqoF2L8yHjbTagH2e2oeMtNyZfr06axZs8Yl\nWby9vYmNjSU+Pp7FixfTu3dvEhMTqV27NjVq1LBW1pYEtWrVyncI6HKw7UNR0KI/+70igoOD8yzn\nqhVRrly5XK0HMFY/gxG7aePGjYSGhnLq1Ckrf+3atXlGpNVoNO7jiikIpZQvMAmY6q42JkyY4BDq\nOj9sYS2++eYbBg8ezIoVK9i2bVu+neGVYvr06cW6cY8zIpLrsJs9tlAcQIEWQkFWRF7Ww8WLF/n6\n66/JzMxk4MCBzJgxg+rVqxMcHExqaiqJiYk8+OCDREVFFWnnPY1Gc3lcyVlMDYF6wDbTv1AbiFVK\nRWBYDDfZla1tph0G6uSSnivTpk2zvkdGRhIZGZmnMC1btiQsLIwnnnjCSps3bx7bt2939XrchoeH\nh9v8EK5iswgefvhhzp8/b6VPmjSJmJgYhyBxNiti8uTJue6IV7Zs2RzWw7lz5/jtt9+sWVU//vgj\nAG+++Saenp58/vnn3HzzzVb0WvsNgjQaTdFZu3at66GG8lpiXRwfDIUQl0feAaCC+b0ZsBXwAuoD\n+wBl5m0EIgAFfA/0zKO+Qi8xf+mllwSQjh07WqEosrOzC13P9Qogb7zxhjz44INWmi1shzMXLlyQ\n8uXLO4QMAcTf318WLlyYo7ytbOPGjQWQbdu2CSBJSUmyY8cOASQyMlL8/f0FkMWLF7v1WjWaGxVK\nItSGUmoR8BvGzKNEpdRIpyJidvqIyA7g/4AdphIYbQoOMAaYD+wB9orIyuKSsXXr1gDW/gfPPfdc\nvkHhbjREhGrVqjlYBTfdZBh68fHxDmW/+uorHnrooRy+iLx8D7aFeCNHjmTs2LG0aNGCpKQkqlWr\nRlBQEHXq1CE2Ntay8NzluLfhvCtgfuS129uVIioqiqZNm1p7cH/wwQdFrsud1zJ9+nS31Ku5crhz\nFtO9IlJTRLxF5CYR+cgpv4GInLQ7ni4ijUQkSER+tEvfIiIhItJYRJ6gGLHF/bf5I5566qnirP66\noFatWiQmJlrHNuexc7TXwYMH89tvvzn4Ipx9D5mZmZaysd3zsLAwa2GjbdtQMFZzT548md69ewPk\nucaiuLjWXgw+//xztm7dyrp16xg/frx1P91BUe/9q6++WsySaK40N/TArre3NwsWLKBHjx6ICNWr\nVy9pka46Gjdu7LAO4uTJkyilSEtL4+jRo2RkZFidk5+fn8OMJnvrYezYsXh6etK1a1dEhNOnT7N9\n+3YrTpQzX3/9NU8//TTt27dn8ODB1h4ZV5K89rAGY4fBqKgoGjVqxH/+8x8AEhISCAoKYuTIkQQG\nBjJkyBBWrVrFLbfcQmBgIL///juQ/x7Yd999N7169SIwMJDx48fnKZttmvKZM2fw9/e3VryPHj2a\niIgIQkJCHPb6rl+/PtOmTaNVq1a0bNnSmjlmzwcffMBtt91GWloaUVFRPPXUU0RERDBnzhxGjhzp\nEDbFZnFFR0fTuXNn+vTpQ9OmTRk9erS1IdSFCxcIDw+3dhJcuHCh5fsbPnw4Z8+epUGDBpYCOnPm\njMOx5iogr7Gna+2Dm/ekvlE5ceKEVKhQQUREMjIyBJCaNWvKl19+KYBMmTJFtm7dKoB06tTJ8kV4\ne3s7+B4Aadu2rVSoUEHi4uLEx8fHZRmGDx8u/fv3l/Pnz8vFixeL/RpFRAICAnKk5bWH9bRp0+SW\nW26RjIwMOXHihFSqVEkyMzMlPj5ePD095a+//hIRkVatWsn9998vIiJff/219O3bV0Ty3wO7YcOG\ncubMGUlLS5O6devKoUOHcsgVGRkpTZs2lRYtWoifn5+8//77Vp5tT++srCyJjIyUuLg4ERGpV6+e\nvP322yIi8s4771h+pWnTpsmMGTNk7ty50rdvX8nIyLDasA8Jb9sX2/l+rV27Vnx9fSU+Pl6ys7Ol\nW7duVjn7e/rXX39JYGCgtQe3Tc7777/fCqX//vvvy7PPPpvr/0fjPriRw31rLg8fHx8r3MYvv/wC\nGENBtjf6jRs3EhYWRrNmzbhw4QI+Pj5MmzaN6tWrO/geOnXqxKuvvkpKSgoDBw601mO4Qrt27fji\niy/w8/PLc19qd5DXHtYAt912G6VLl6ZSpUpUq1aNY8eOAcabui20S3BwMLfeeisAISEhJCQYwQPy\n2wO7a9eu+Pv74+3tTbNmzaxznFm0aBHbtm0jISGBN954w1p5vnjxYlq1akVYWBg7duxwqPuuu+4C\noFWrVg4+pIULF7Jy5UqWLl3q4OsZOHCgS/cpIiKCunXropRi8ODB1hoasQs0uXr1avr3729FRbat\n5B81apS1i99HH33EyJHOrkpNSaIVhCZffH19SU9PZ9u2bQwYMAAwdpyzKYhVq1YB0LlzZ0uRjB07\nlu3bt1O6dGk2bNjApEmTSE9PtwIEpqSkFMop/Mgjj1htX27gwsIgYuxhvXXrVrZu3UpiYiJ+fn6A\n497UHh4e1jCbc7rt2L7MlClT6NKlC3FxcXz77bcOw2e57Xmdl2xgbBkbHh5OTEwM8fHxzJw5kzVr\n1rBt2zZ69+6da93O9bZo0YL4+Pgc4U3sJxyULl3aGtYSEStAI+T03xTGn9O+fXvi4+OJjo4mOzvb\n5b3WNVcGrSA0+eLh4YGnpyezZs0iOTmZN954g/Lly1vKoGrVqoDx5mx7u1ZKWSvBP/vsM1atWkVa\nWho+Pj4899xz+Pn5FXqluK1z2rt3r0PnVFzYv+3ayGsP68LW40xqaupl7YFt38758+fZunUrDRs2\n5PTp0/j7+xMQEMCxY8dYsWKFS3WFhYXx3nvvcccdd5CUlJRrmXr16lk+lK+//tohFMumTZtISEgg\nOzubJUuWWJtreXl5Wf6ELl268MUXX3DypDEvJSXlUhSe++67j3vvvZf777+/kHdB4260gtAUiI+P\nj7Wyu3nz5vj4+LB161aqVKnCsWPHKFOmDO3bt88R+VVEWL58OYcOHSI9PR1vb28CAgJISkpyOcCf\nDfs33uHDh1/+RTlx4cIFbrrpJurUqcNNN91kbdiU2x7Wzti/Mef13Z7nnnvOpT2w83sTHzp0KGFh\nYbRp04b777/fCmkfGhpKUFAQQ4cOpUOHDi7VBcab/IwZM7jttttITk7OUf7BBx8kOjqasLAwNm7c\n6PD/a926NY899hjBwcE0bNjQWvz40EMPERISwn333UezZs2YNGkSnTt3JiwsjGeeecY6f8iQIZw6\ndYpBgwblK6OmBMjLOXGtfdBOardRrVo1iYyMFEAOHjwoo0aNEkDuvfdeq8zJkycFkEcffdTa6zs2\nNlZq1aolnp6eUrt2bdm/f7/MmjVLAJk0aVKhZHj66aflpptuEkCeeOKJYr0+TdFZu3at3H777ZdV\nxxdffCHDhg0rJok0hQXtpNZcDjVr1rScsLVq1bLeeu2nUfr7+1OmTBn++9//sn//fgDCw8MJDAyk\natWqHDp0CG9vb8tpW9C+EM7MmDGDv//+m5dffrnQ1ofm6mXs2LFMmjSJKVOmlLQomly47naU0xQ/\nrVu35sMPP2TBggUopXj++efp1q0bjRo1ssp4enpy4sQJfH192bVrl4Mzt2rVqhw+fBhfX19q1arF\ngQMHCowm64xSilKlSlG2bFk2b97MK6+8wr/+9a8iX5OI5Fhb4e3trWM+FZLOnTvTuXPnIp8/Z86c\nYpRGU9zY4h1d8yil5Hq5lquNtWvXEhUVxbJlywqcZnrLLbfw22+/Wcd9+/Zl5cqVpKWlueTALYgv\nv/ySu+++G3DNIWzj5MmTDgEGlyxZwrJlyxxWebdt29ZhbLxt27ZWaBGN5npFKYWI5Oqk0haEpkA6\ndepEvXr1KFu2bIFlnWcneXt7s379epfDsBdEv379ePvttxkzZkyhztu0aRMDBw50kE8p5eD83r59\nO6NGjQIMp/XMmTMZO3Zsscit0VyLaAWhKRAPDw/Wrl3r0g57zgqidOnShIeHF6s8PXv2zHWXwNx4\n6aWXAPjXv/5F/fr189w+FXAIShgQEKCnXWpueLSC0LiEqx1yjRo1HI4rVapU7LL4+vrmmFKbG5mZ\nmTz//POAMY3z9ddfZ8CAAQVut+rr68uECRPctqufRnOtoD1ymmKlXr16DsevvfZasbfhqoKw3/zp\nrrvuYt26dQUqBzCsHj20pNFoBaEpZlq2bOlwbAuvUZzYFMTq1atzDUWRlZXFrl27CAsLs9KUUsye\nPdv6nl/d2nrQaAy0gtAUK7ZNmMqVK+e2Nry8vMjMzKRr165s2bLFIU8pxeTJkwkKCrLSvv32W06f\nPm0d5zf7SVsPGs0ltILQFCu2aJ22yKHuQCll7YKWlJTEsmXLgEtO5qNHjzqU79Onj8Nxjx49rP0T\n7PH19WX8+PHaetBoTPQ6CE2xs2LFCtq1a2cpC3fRu3dv/vnnH7Zs2YKIEB8fT/369Rk6dCiffvqp\nVU5EHIaVtm/fTmhoaI7hqYCAAI4cOaIVhOaG4cyZM5QtW1avg9BcOXr16nVF2klOTraGmESEBQsW\nAMZOcDZs28h+/vnnlC1blqNHj+Lv759DOWjfg+ZGwvYybf8ilRvagtBcs3z11VfWUNbvv/9u+T/s\nycrKyhE+459//nHY/xqMvQ+SkpK0gtDcEAQHB7Nv3z4rdH5eFoT2QWiuWfr27cuIESMAeP/997nt\nttusdReNGzcGyDW2ki1OFBj+jNKlS9O6dWutHDQ3BNnZ2ezYsYNFixZx+PDhfMvqISbNNc0777zD\nggUL2L59O4GBgSQkJODv78+QIUPynGJrn27zT9SvX/9KiazRlBgXL15k3LhxhIeHWzHN8kMrCM01\nja+vL506dWLv3r20bdvWCgWekpLC1KlTcz3HeQZTp06dOHXqlNtl1WhKkgULFlh7ftv76fJDDzFp\nrnmeffZZjh49aikHT0/PAqOw2pzXYPgfjh8/7lYZNZqSZs2aNXTv3p3w8HCXQ+BoJ7XmmicjIwMv\nLy8ef/xx5syZk2Naa17YyrRp04bU1FR2797tblE1Grexc+dOEhIS6Nmzp0O6iLBx40b69OnDhg0b\naNKkiUN+fuG+3WZBKKXmK6WOKaX+tEt7XSm1Uyn1h1JqmVKqrF3eRKXUXjO/u116uFLqT6XUHqXU\nbHfJq7l28fT0BC6Zza4oB4AHHniAKlWqcPToUW1BaByQS1sZXzO88sor9OrVi8WLFzuk//HHH7Rv\n357Q0NAcyqEg3DnE9BHQwyntRyBYREKBvcBEAKVUM2AAEAT0At5Rl37l/wVGiUgToIlSyrlOjQYw\nQnAUhg8++IDmzZtz6NAhUlJSWLNmzTXXKWiKFxEhPT2defPm4eHhQXp6ekmL5DK2bYEHDx7skL5/\n/37uuusuhw2zXMVtCkJE1gEpTmk/iYgtnOZGwLbv5B3AYhHJFJF4DOURoZSqDgSIyGaz3EKgr7tk\n1ly7/P3330XavnLNmjXW9y5duhQ47U9zffPvf/+bMmXKMH/+fAArwOO1QGpqKtOmTQMc442lpKQU\nOapBSTqp7we+N7/XAg7a5R0202oBh+zSD5lpGo0D9evXd2nHu4Jw3qdac2MRGxtLVlYWMTExtGzZ\nkgkTJjBv3rySFsslUlNTGTRoEP7+/pw5c4bz588Dl6cgSmSaq1LqX0CGiHxenPXatCdAZGQkkZGR\nxVm95gbA9qPS3Hj8888/xMXFAdC5c2cCAgLYtm0bDz74IA888EAJS5c/IsKJEycoX748ISEh1KlT\nx4pgPH78eAcFsXbtWtauXet6xe76AHWBP53SRgDrAW+7tAnAeLvjlUBboDqw0y59EPDfPNoSjaaw\ndO3aVQDrs2HDhpIWSVMCnDlzRtq0aSNDhgwRQL7//nuJioqynovs7OySFjFfdu7cKfXq1ZPs7GyJ\nj493eKabNGki77zzTp7nmn1nrn24u4eYlPkxDpTqCYwD7hARe+/PN8AgpZSXUqo+0AjYJCJJQKpS\nKsJ0Wg8DvnazzJobiIULFwJQvXp1QFsQNyr9+vVj8+bNPP7445w7d45evXo5+KNc2cHwSvP999/z\nySefEBkZyVNPPUXz5s1RSlG5cmWHcnv27Cny5At3TnNdBPyGMfMoUSk1EvgP4A+sUkrFKqXeARCR\nHcD/ATsw/BKj5dIVjQHmA3uAvSKy0l0ya248vL29AcNZ3aFDB60gbkC2bt3K5s2bady4Ma1bt7Zi\ndS1ZsoSffvqJKlWqcObMmRKW0pHly5dz2223MWzYMCpWrIiXlxeTJ08GLsUaCwkJsaIGFHUDL71Q\nTnNDY4uHn5CQwDPPPMOAAQPo379/SYuluQL85z//4cCBA8yaNYvRo0fz9ttv51quQYMG/PDDDxw9\nepROnTpdYSlzx36tT2793meffUavXr0oX748mZmZeHp65rk+qEQWymk01wK2tRM+Pj74+fkxb948\nvRbiBmDZsmWMHTuWWbNmATjsX+5MgwYNiI2NpXPnzlfFuoiYmBgAbr755jzLDBkyhIoVK+Lh4YGX\nl5fLi0ed0QpCc0NjW4Xt5eWFt7c3P/74I3v27ClhqTTuRES45557ABg5ciSpqan5zlKqV68eX39t\nuD5ti9FKinXr1tGuXTumT5/Op59+ynfffefW9nQ0V80NjW2/CC8vL0tZHDlyhMDAwJIUS+NGDh40\nllwtX76c3r17F1je19fXWjiXlJRUYCBIdzJz5kwAJkyYABjWjTvRFoTmhufUqVP4+flRurTxvqQX\ny10b7N69m08++YTw8HCXnch79uyhR48e3H777S4pB3DcP6QkwsJnZWWxcOFCtmzZQunSpVm0aNEV\na1tbEJobHtsMD5uCuBrGmTX58+GHHzJq1Cjr+O+//6Zly5YFnvfll19St27dAvditsc2K0gpRUpK\nSgGlixcR4fXXX2fSpElWmm0XxSuBtiA0GhNtQVwbpKen88YbbzikHTp0KI/Sl5gxYwYTJ05kwoQJ\nhQrLYrMg6tWrx7/+9a8r+nzs27fPUg4RERFA4YNSXg5aQWg0JrY549qCuHoREVauXImfnx8bN25k\nzJgx9O7dm5MnTxZ47rp163jzzTcLHYLHpiDGjh3L/v372bt3b1FEz4Ers+V+/fVX63tMTAzR0dF0\n7NixWNp3Ba0gNBoTPcR09SIi3HXXXXh4eDBw4EBGjBhB27ZtmTt3LnXr1iU1NbXAOk6cOEHr1q0L\n3ba/vz8A9913H1FRUfzzzz+FrsOe+Ph4lFJ4eHjw3nvv5Vnu7NmzOaLJdurUCR8fn8tqvzBoBaHR\nmOghpquX7du3s3XrVp599lnS09Pp2/dS1P9y5coVqCBSUlKIi4ujfv36hW7bNhxVoUIFqlWrxtGj\nR3Mtd/jwYZc2nvrmm2+s73/88Uee5Xr16kVcXBwHDhxg3bp1hZS6eNBOao3GxDbNVSuIK4+IkJmZ\nSXZ2Nkopa5z9wIEDvP/++3h4eDBgwADLAqhTp451brly5QocYnrrrbfo1asXtWvXzrdcfnh4eBAa\nGkpsbCxDhw51yNu9ezdNmzalWrVq7N+/39of3Zlu3bpx7tw5xowZQ7t27VixYkWu5dLS0oiLi+PQ\noUPUqlWLevXqFVnuy0ErCI3GxGZB2LYu1Vw5nn76aRYvXkxSUhLt2rVj48aNbNiwgdmzZ7NkyRIA\nfv/9d8LDwx2sB4Dy5ctz4MCBXOvt168fPj4+rF27Ns/OuCBat25NeHg4AJUrV851IeWBAwdo3bo1\n6enp7N692ypvz+7du/npp58AeOaZZ/Dw8ODcuXM5yl24cIE1a9bQsmVLatUq2e1vtILQaExGjRrF\nypUrSUpKKmlRbijS09P59ttvrfu+ceNGIGcoifDwcAfrwob9EFNERATPPfcc99xzD0lJSXz//feW\nT8mVabC5Ua9ePbZs2QIYIVnS0tIYNWoUgYGBvP766xw7dowzZ85Qt25dTp06RXJycq71fPXVV7Ro\n0YI///yT4OBgEhMTc1UQTZs2JTAwkKioqCLJW5xoH4RGY1KxYkXGjx+f5xizpuiICE2bNkUp5TBO\nLyL4+Piwf/9+gFynn7Zt25YyZcrkGU+oXLlyLFmyBKUUmzdv5uOPPwbghx9+oE+fPgCsWrWqWK7D\npiA+/PBDxo8fT3JyMufOnbOCPlaqVCmHE/uFF17gnXfeYdu2bTz11FMsW7aMwMBAypQpw7lz59i/\nf791T9LS0khMTGTVqlXccsstxSLz5aAVhEZjR/Xq1bUF4Qbi4+PZvXs3AFWrVrXSbYHnwFiQ9uST\nTwIQHR3N0KFDGT58OMuXL+fvv//Os257fwRgzfJZsWIFvXr14uOPPy62KKw+Pj5s27bNIe2vv/5i\n1KhRlC5dmjZt2rB+/XqH/GnTpjF+/HgSExNp0KAB/fr1QylFmTJlOHv2LI0aNbJCdR86dIgqVaoQ\nHBxM27Zti0Xmy0EPMWk0dmgF4R7yCnL3559/AjB79mwee+wxSpUqxdSpU/Hw8HC5Uw8JCUFE8PX1\nJS0tjXLlypGVlcWqVauYOXNmsY7je3l5WdaOjR9//JEWLVowceJEYmNjcygIMJ6r9evXU758eSut\nUaNGlu/ENtR08OBBmjZtSnR0dJEjsBYn2oLQaOyoXLkyqampXLx4saRFua7YuHEjbdq0YeLEiQBk\nZmYCxvTVN998kyeeeMJaqGgLoFhYbLPP0tLSGDhwICdPnix2J29uM9z++ecfOnbsSP369alUqRJf\nffWVNWRkW+ewb98+wHHjHn9/f2tWlu3ajx49Ss2aNa8K5QBaQWg0Dnh4eFClSpXLXgx1o5KRkUFy\ncrLDArDExESeeuopEhISePXVV2ndujXR0dGAoSCaN29eLG2HhobSuHFjzp07x7JlyxgwYECx1GuP\n/SJK25qK48ePW9NabVvXDhs2DDAso1deecU6x9nH8s0337B06VK2bt1KRkaG5cu4WtAKQqNxonr1\n6tpRXUTCw8OpXLkyjzzyiNWZLl++nGHDhlnDKQMGDODWW28lJiaGrVu35rtZT2HYunUrM2bM4Kuv\nvgKwNgMqTu68805r0drzzz8PGBaEbbV106ZN2bhxI8ePHyczM5MDBw5YjnLIqSACAgLo27cvtWvX\n5qGHHiIuLi7PNRQlgVYQGo0T2g9RdGrUqGEFlfPx8eHTTz8lNjaWtm3bWlFRe/XqBcCkSZOoUqUK\nlStXLrb27VdK16xZs9jqteHp6cktt9yCiDBixAiGDh1KdHQ0u3btssoEBARw7tw54uPjqVGjBkFB\nQXz22WeIiDWUZE+pUqUYNmwYCxYs4O2339YKQqO5mqlSpYpLIRM0OcnMzOSVV16xFootX76cH374\nwSHAnG0WU2pqKhUrVizW9kNCQnj//fe58847i7XevOjZsyeAg/PZz8+P8+fPc+LECapWrYqnpyf3\n3keF5woAACAASURBVHtvvvXYn68VhEZzFePj46Od1EUgMTGRbdu2ERISYu3Id/jwYZKTkx38DLbO\n8PTp01SoUKHY5XjwwQetYSZ3c+uttwIwd+5cK61MmTKcP3+ec+fOudzZ2w+z2SytqwE9zVWjccLL\ny0sriEIiItStW5dx48ZRrVo12rVrx9mzZ9m8eTM1atRwmJVjWwm9d+9ehgwZUlIiFwvVqlXLEbbb\nz8+Pc+fOFUpBVKtWjZUrV1KjRg2aNWvmDlGLhFYQGo0Tnp6eWkEUElv8qpdffhkw9k7o0KEDrVq1\nsoZh7BkxYgQxMTGMHz/+isp5JfDz80MpxbFjxwplDfTo0cONUhUNPcSk0TihLQjX2LlzJ6NGjSIr\nK4u9e/cSERHhECfJNrOna9euOc59/fXXWbVq1RXd2+BKoZSiXr16/Pnnn1eVP6EoaAtCo3HCy8uL\njIyMkhbjqmbOnDk88cQTgDEOf/z4ccvvYKNu3bpA7gqiSpUq7heyBImKiuKDDz7gzTffLGlRLgu3\nKQil1HygD3BMRFqYaRWAJUBdIB4YICKpZt5E4H4gE3hCRH4008OBBYAP8L2IPOkumTUaMBTE+fPn\nS1qMq5odO3Ywbdo0QkNDmTp1KklJSXz33XcOZby9vUlKSqJatWolJGXJ8fjjjxMYGMijjz5a0qJc\nFu4cYvoIcB5UmwD8JCKBwGpgIoBSqhkwAAgCegHvqEterf8Co0SkCdBEKXX1DdRpriv0EFPu7N+/\nn59++okZM2Zw8uRJAgMDadSoEdu2bSMiIiLX7TxvROUAEBgYyOOPP37VhMwoKm5TECKyDkhxSr4T\n+Nj8/jFg2/njDmCxiGSKSDywF4hQSlUHAkRks1luod05Go1bsDmpL1y4kCNv4cKFLm02f70RHx9P\no0aN6NatG+PGjSM5OZmKFStaU1YbNmxYwhJq3MGVdlJXFZFjACKSBNji/tYCDtqVO2ym1QIO2aUf\nMtM0GrdhG2Ly8/Pj9OnTVrqIMHz4cM6ePVuC0pUMI0aMcDjev38/DRo0sBTE5Wzlqbl6KWkndbG+\nik2bNs36HhkZSWRkZHFWr7lB8PHxsaZt7ty504rLbxt2utEc2CKSY5vNI0eOUK9ePSt0hFYQ1w5r\n165l7dq1LpW90grimFKqmogcM4ePbCEzDwP2u37UNtPySs8VewWh0RSVmjVrWuGZ09PTycjIwNPT\n0xpyso/oeSPw22+/UaZMGbKzs9m0aRPPPPMMJ06csPbw/uuvv2jcuHEJS6lxFeeX5xdeeCHPsu4e\nYlLmx8Y3wAjz+3Dga7v0QUopL6VUfaARsMkchkpVSkWYTuthdudoNG6hdu3a7N27FzCGVho1agRw\nQyqIuLg4evTowaBBg1BK0bZtW1q2bEmTJk2sMs2aNcPT07MEpdS4C3dOc10ERAKVlFKJwFTgNeAL\npdT9QALGzCVEZIdS6v+AHUAGMFoueQLH4DjNdaW7ZNZowBhisikBW4hquPEURGpqKu3bt+fcuXME\nBQVZ6T169ODw4TwNec11hNsUhIjkFb7w1jzKTwem55K+BQgpRtE0mnyxDZ04s2XLFuDGURD79u2j\ncePGjPr/9s49vqryyvvfRQJJDAghRnlFCCB3OqiAqICVqgVvULVOwXHQqtNKwdZL7aAOKlatWqde\nRlspin31o1C1IwrWuxUUBwFRURCoL6ORCgQBDQGiIWS9fzzPPtk5OZCL57KPWd/PJ5/sy7P3Wee3\n99lrr+eynosvrpcGYvz48Rm0ykgnmW6kNozIsa/qkmCGstbiIL788ks6derE1KlTM22KkSHMQRhG\nHPEOQkRQVXJzcxk0aFCrGUQXOAij9WLJ+gwjjvgqpvbt21NZWUl+fj5FRUWtJoK49957W+WgQKMO\niyAMI474CKJTp05s376dzp07k5eXl9UOoqysjNLSUhYtWkSXLl0aJNgLqKioYNmyZaxZsybNFhpR\nwiIIw4gjPoLIzc3lqaeeYsOGDVntILZt20aPHj0QEUaPHr3f/u/Tpk3j1FNPjWVkNVonFkEYRhzx\nEUR1dTW//OUvAVrkIPbu3cvf/vY3ioqKEia0Sxfvv/9+vfX9zbs9Z84c/vKXv6TaJCPiWARhGHEE\n6SMCwo3Sr732GhMnTmzW+W655RbGjBnDhAkTkmJfU3j00Ud5+eWX621btWoVAPfffz8bNmyo5zA+\n/vhjvvrqK8A5tN27dyecx8FoXZiDMIw44lM0V1dXM2nSJO68806+/PJLADZv3tzk823btg2A448/\nPnlGNsKkSZM4++yz621btWoV9913H5MnT6Zr167U1NTwxhtvsGPHDnr16hWbHrOyspLCwsIGjtJo\nfZiDMIz9kJeXR3V1NSJC586dYw/N559/vsnnKC8vZ9SoURQUFKTKzHrs2rULoEHW2Q8//JCBAwcC\nzgmKCN/97ndjyQiDHkuVlZWx6UKN1o05CMPYD4WFhVRVVbFnzx5yc3Nj7Q8XXXRRg1nnampqWLp0\nKbNmzaqXq2jz5s0cfvjh/PWvf02LzVu2bIktb9++nRUrVrBu3ToWL15M9+7dY/uCSGn79u3ceuut\ndOvm8mJWVlbSoUOHtNhqRBtzEIaxH4IpI+fOnUtubi75+fmxffEpsOfOncuxxx7LypUrY8n+ADZt\n2kRRUREbNmwgHZx33nmx5fPPP59hw4bRv39/wGWqDVi6dClDhw5ly5Yt9OzZk8rKSsB1cT3wwAPT\nYqsRbcxBGMY+mD59OjfffHNsPTc3l9WrV3PttdcCrjEX3PwQ1dXVsV5B8ZFFeXk506dPB0jLKOy2\nbdty8skn89Of/pTly5fX2xeu5urVqxdnnXUWQMxBzJw5k9WrV8ccitG6MQdhGAlYtWoV06ZNq7ct\nJyeHXr16xerxa2pqABg5ciRnnHFGrAF77ty5ANTW1lJdXc2uXbsoKiqipKSEL76In4U3+ezZs4fr\nr7+empqaetVNiQiqnI444ghOOeUUfv7zn7N8+fKMdsc1ooM5CMNIwKBBg2INtZMnTwbqBtDl5eUB\ndRHE8uXL2bFjR6xnU1B//+abb7J161aKi4tp06YNXbp04R//+AepZv369fTu3ZsFCxbU275kyZIG\nZc855xyWLVtGXl4ezz77LAUFBbz66qvmIAzAHIRhNEphYSFQ5yCC/8G4AXCTDJWVldGxY8fYdKUP\nPPAAW7dupaSkBIAhQ4bwzjvvpNTWyspKdu7cSZcuXZgyZQrgei+tXLmSY489tkH5goICjj766HrH\nr1+/niOPPDKldhrZgTkIw2iEICIIHEPQ+2f37t312iE+/fRTKioqABgzZgxr165lzZo1MQcxbNgw\n3n777dh5y8rK+OSTT5Jq63PPPcfBBx+MiDBjxgyqqqoYMGAAgwcPbtLxN910E0cddVRsTITRujEH\nYRiNEOQjChxEmzbuZ7Nly5bYmIP58+ezdu1aZs2aBbioY/ny5UycODE2HmH48OHMmjUr1ltoyJAh\nDBw4kE8++YRJkybx6aeftsi+9957j3nz5rFnzx7uuecejjvuuNi+cK+rpjB9+vSURzlG9mAOwjAa\noU+fPkBDB/HBBx/EHETAhAkTePPNN7n99tsBOPfcc3nssccA5yBycnJiDdU7duygqqqKxx9/nEcf\nfbTFmVPnz5/PI488woknnsiqVau4+OKLW3Qew4jHkvUZRiMEg96CUdSBg7j77rsZOnRovbKFhYWM\nGDECgMWLFzN48OB6g8769u0biyhycnKoqamJVUuF2zSaQlVVFVdeeSUzZ85k6NChbNq0icrKSoqL\ni1vwLQ2jIRZBGEYjHHTQQQCxN/+OHTvG9j399NP80z/VTZkezl80cuTIBiOSc3Nzef3114E6RxN0\nj62qqmqWXcuWLYtVaa1YsYKNGzdy4YUX0rNnz2adxzD2hTkIw2gEEWHIkCGxqqYRI0ZQVlYGuId7\nfBSxPz744IPY6OygsbuiogIRaVYEUVNTw65duzjssMPqbX/ooYfqOTDD+CaYgzCMJrBixYqYgwA3\nwGz8+PFs3Lgx9sZ++umnN3qeIFX4+PHjYyOu58yZQ/fu3ZvlIE488UROP/10ixaMlGIOwjBaSF5e\nHhs3bqRLly4AjB07ttFj5syZA9BgENvXX3/Nz372s0bngP7444957733Yt1ji4qKqK2tBWDq1KnN\n/QqGsV/MQRhGC2nXrh0VFRWxNoqmEJ5r4qabbootB9FEeXn5fo8/7bTTOOqoo2LtF5WVlYgIDz/8\nMFdddVVzzDeMRsmIgxCRa0RktYi8LyKPiUg7ESkSkZdEZJ2IvCgiHePKfyQia0RkTCZsNox4gpQb\nzXEQQKyX0a9+9SvOPPNM7rnnHpYsWUJxcTHr1q3b53Fbtmxh7dq1ALGeT0H0cP7559OjR4/mfgXD\n2C9p7+YqIqXAT4D+qlotIo8D5wIDgVdU9bciMg24BrhaRAYCPwIGAIcBr4hIH20sFjeMFNOuXTug\nzkE0dZKdkpIStm3bRl5eHvPmzYttHzduHB999BEnnHBCwuPCziMYbNe1a9cW2W4YTSET4yB2ANVA\noYjUAgXAZziHEPwyHgYWAlcD44E/q2oN8ImIfAQMB5am2W7DqEcQQZSUlLBmzZp6jdj7o6SkJBYJ\nhOnTp0+9eSTiCacKz8/PZ9GiRfTq1auZVhtG00l7FZOqfgH8DvgU5xgqVPUV4BBVLfdlNgMH+0O6\nAuGZVj7z2wwjowQjq4uKiujfv3+T53AOBtLF06dPnwaTEIUJ5rYGN/HP0KFDKSoqaobFhtE80u4g\nRKQXcAVQChyKiyTOA+KrjKwKyYg0wcC2wFE0lVtuuSVWRRSmsQhi27ZtXHLJJQwcOLDe1KGGkSoy\nUcU0DHhTVbcDiMg8YARQLiKHqGq5iHQBgplOPgO6hY4/zG9rwIwZM2LLo0ePZvTo0Uk33jACggbi\n5pKTk5OwvaJ3796sX7+e2traWC+lgHnz5jFlyhSuvfZa+vTpQ+fOnVv02YaxcOFCFi5c2KSymXAQ\n64DrRCQf+Bo4CVgO7AR+DNwOXAA848vPBx4TkbtwVUu9gWWJThx2EIaRapLdT6J9+/Z069aN559/\nvsGgu6DNori4mO985zuWjttoMfEvzzfeeOM+y6bdQajqShF5BFgB7AXeBWYBHYAnROQioAzXcwlV\n/VBEngA+BPYAU6wHkxEFUnEbnnXWWaxcubKBgwgijs6dO3PppZfWG09hGKkiI9lcVfUO4I64zduB\nk/dR/lbg1lTbZRjNIRUOom/fvjzzzDO89dZb9WaACzLAtmnTJta91jBSjY2kNowWkgoH0a9fPxYs\nWFBv0h+oyyTb3AmADOObYA7CMFpIqhxEIr744gsuu+wyfvjDHyb9Mw1jX5iDMIwWkgoHEU7bEWR3\nXb58OQ8++CAjR45s8lgLw0gG5iAMo4UMHz6ctm3bJvWc4cbnYM6J2bNnAzB48OCkfpZhNIY5CMNo\nIZdcckm99BfJZuvWrQB8/vnnPPnkk/usfjKMVGEOwjAixhtvvEFpaWnMQezcubPJiQANI5mYgzCM\niDFq1ChOOukktmxxyQR27dpFYWFhhq0yWiPmIAwjgnTr1o0NG1yOSosgjExhDsIwIkhpaSllZWXs\n3buXTZs2ceCBB2baJKMVYg7CMCJI9+7dWbFiBe+88w6qavM+GBnBHIRhRJDS0lJWr17N8OHD6dix\no+VeMjKCOQjDiCDdutVluLf0GkamMAdhGBEkmM4UzEEYmcMchGFElMsvv5w2bdpQUFCQaVOMVoo5\nCMOIKGPHjqW2ttYiCCNjyLdl7h0RsXmEjG8VZWVl9OjRg5ycHGpqajJtjvEtRURQ1YS9IDIyYZBh\nGI1TWlrKvHnzWLNmTaZNMVopFkEYhmG0YvYXQVgbhGEYhpEQcxCGYRhGQsxBGIZhGAkxB2EYhmEk\nxByEYRiGkRBzEIZhGEZCzEEYhmEYCcmIgxCRjiLypIisEZHVInKMiBSJyEsisk5EXhSRjqHy14jI\nR778mEzYnCwWLlyYaROahNmZXLLBzmywEczOdJKpCOIe4DlVHQAcAawFrgZeUdV+wN+AawBEZCDw\nI2AAcCrwB8ni5PjZctOYncklG+zMBhvB7EwnaXcQInIgcLyq/glAVWtUtQL4AfCwL/YwcKZfHg/8\n2Zf7BPgIGJ5eqw3DMFofmYggegJbReRPIvKOiMwSkQOAQ1S1HEBVNwMH+/JdgQ2h4z/z2wzDMIwU\nkvZcTCIyFHgLOE5V3xaRu4BK4FJV7Rwqt01Vi0XkXmCJqs7x2x/EVU89FXdeS8RkGIbRAqKUzfUf\nwAZVfduv/zeu/aFcRA5R1XIR6QJs8fs/A7qFjj/Mb6vHvr6gYRiG0TLSXsXkq5E2iEhfv+kkYDUw\nH/ix33YB8Ixfng9MFJF2ItIT6A0sS5/FhmEYrZNMzQfxC+AxEWkL/C9wIZADPCEiFwFluJ5LqOqH\nIvIE8CGwB5hieb0NwzBST1bOByE2+YNhGEbKyZqR1CLyAxH5u4j0jLJzEJF+vg2FqI7XEJFTRORk\nESn065G0E0BEcjJtQ2OIyAkicoiIZM3vKcqIyGgR6ZoN1z4bEJFDW3xshJ+1AIjIIOAWoAAoBh5W\n1Xsza1VDRKQ9cAPwU+A+Vf2PDJvUABHpDtyJ6ya8CThAVU+JYkTm9fwNrupxhao+lGGTGiAihwF3\n4zpRLANqVfWyzFqVGK/nfwCbgVdVdVWGTWqAiBQDs4D+wApgt6pOzqxVDfFa3ojT8kVVfT/DJiVE\nRPoAjwLlwPWq+l5zf+uRfuMRkQHAtcBLqjoW+BNQ6/dF5q1XRH4CLMS16dyP72UVJRs93wN2qepx\nqno2cIiIdFZVjZKtItIb+B/ctV4ATBGR4zJrVUKOB3ao6jG4drWxInJBhm1qgIj0ABYD+cBO4AYR\nOd3vi9IzYCCQp6qDgCnAMSLyIxHJVFtpA0SkBHgZyMM9P28TkTP8vsho6aOvk3BtvB8Ax4tIu+a+\nCEbmC4Xx6TXApeA4X1X/4NdLcOk2okZn4AJVvQJYBUwCiMJbuXeyAcVAlYj0EZHrcG8Wp0I0bA0x\nAHhBVS9X1RdwzmJdhm0CGujZE1glIgVev1eAqb63XZToj3vTvUJVZwNLgN8BqGptRi2j3oN1L/C5\n7+6+E7gZmACUZsy4huQDq1T1UlW9HZgN3AvR0DJAVffihhD8C/AervfnqOaeJ1IOQkS+JyLLgD+K\nyLXAcFXdKyL5vsjDQJGIdMvkA01E+orIT0TkaABVvV1VV/vdC3EP4UGZsg/qaTlLRKb7bsV/xr1N\nzMFFE3cAl4nI1Zl8SwvpOcxvqsK98UwRkRdwD4mbRWSqL5+JFDHxeh6Oe9B+FzhXRE4E2uMecqf6\nYzISlYX0HOo35eJ7BXq24n5H03z5TOh5mm9TPDb0YG0DCO6FC1X9b6AGGJdBO0t91WzAQUDf4Pfi\nbfxYRH6dKRv958b0DG3e5p+TLwEVwAgROaQ5dkbKQQDnAA/i8jBV4R4KB6jqV35/IS4XU2GG7ENE\nfgC8CQzCPSwmikiHUJECYCPuxs4kYS13A78HKlT197go5xeq+ipwCfBvOLvTTpyeD4jIuar6CnAu\n8B3gY6AL8Bhwi4gclKE3tfh78w/AG7g3yMG4qtD7gGm4vGIZicri9HzQ6/ks8KGI/F8R+SNwBnA5\nMM7/vtKqp3+x+jFuMGysrU5VF/vFU0SkyC/PxN2faX1DF8eNuMj1TyEb38U5satCxS8FfigiB2bi\n3tyPnrW+zaECeB04BDg62N2kk6tqxv5wDirPLx+EGxTX06+3w12Y/wyVz8G9AX8vOD4D9t4BjPPr\nZ/r1yXHl3gXOS6eNTdTyt355Pi46C459FuiWoesfr+fvAj2BecCgUPk/AxMioucjwG3Beui4ccDV\nflkioOfduAgsD9dmcjWuqrYLrkG4Yzrs9Lbl++ViYIBfXgmcGyp3DPBkcJ29nX8MrkUateyA69Bx\nHPAcMCm07yhcFNbRr+cDDwH903yt96snzpFJ6JiL/TNgQXCPNvaXsQhCRC7DNZz9QUT6qepWXBh0\nBYCqVgO3A6N9OI+6erWF+EyvmgZvLSLDRaS3iBT6zysAvu93vwC8DQwVkX6hw2bj6/vSZGNTtTwZ\nVw3yFnCpiNwnIu/hBiE2SF+SIlsb03MZcLSPyjYD1/vjjgcOxemdahuboudvgJNF5HBVrRaRtiLy\nc1y9+WZfLuURRBP0XOLXe6rqG6p6m6p+jqsaE1WtSLWdIT1/LyJ9VHWbqq7xu28EpolIvn/bXQo8\nDpwmIo/j7tXNqvp1Km30dgZatlfVSuAOVV2C+z3/wjf8oi6K+Atwr69KHgX0wqURSjlN1VM9Utdd\n+ChcjUEF8ECTPiydXjnkyYbhegIcjnsAzMGJ3B2XduNwX+4AnBcPv2GMAbqnwcZ8XJXBJ7iqhQV+\n+wm4rmP9/Hpf4NfAxNCx04Hr8B4+Qlreg0ufnoebh2MGcGSarnlz9PwNcDquKvF5XCTxdljjiOh5\nZ2ATMAJ4mlBkFiE9fx2ysycumlwPnOy3pSyCSKDnI8BpcWVeAGbEbeuIS7mT8vszTsvZwDNx+3Nw\n0etNoW0FwJXAU8D7wI/SdN1bquf/8b+lcM1Bo7UbKf9CIWPCoc5EXF9scGHQlbg33FLcw/XFUNnZ\nwOh02Rn63N6BjX59ETAV6OcvzI2hfbOAS8IXI8Jafi/R+Zpys6RZz6l++YDgYRdlPYG2cXqmtNqm\nBXoG1Xb9Uv0wa4Ket+KrRPz2vjjnOwL3cjAo/nypvD8TaLkQFy3mh7YNx1Vvt/PrHfz/LqnUMkl6\nDog7X5um6pmWKiYRuQa4U0TG+U3LgU9FZLA6i1/CeelRqnoz0ElEbhaRu3EXZnea7OwTWlVgS2jb\nv+N6p3TA3UADRWSS31eBewtxB6pu8udLur5J0HJX3PlEHUmvCvuGerYFUNXdqrrOny/pI2uTpaeq\n7vHna+P1THq1TTLuT1Vdp6pP+PNlQs8Xcb2qjol9EdW/46o+Xwb2aF2PwJTdn03Q8vu4hv7AhmW4\nbqPvisib1E1aVu7LpGTUdxL0rNG66qfg/qxtqp4pdRAicrSIvIvz0GtwfcR/DHyOa3EP6ulX4Ub2\nDvaHngcsxX3xU/zFSaWdw0TkJWC2iPxWRIbj5qgA6ByqG/1/uLev13Fh+lQRWQyciGv4qUcyb+pU\naZmiB1ky9Jwff151bVDJsjFVeqbC0X6b9FyN07O3P66juC6iK4DeqnpDnI1JvT+bqOUy3Bis8wIb\nxI3NOgP3QnCduh6AMfuSqaW3M1l6Xh8+b7PvzxSHRj8g1OsEN2jjv/zyv+LqcMf69aNwDVINQnNS\nG16e4MWcgOutMgP4jd93m/87xK93x3W7PNivlwBHp1LDbNLS9DQ9m6HnkkA7oDh0XE4inTOoZbFf\nvwC4KHSuVFchRkLPlAyOCsJC3MjSdqFdhwJBb4SXcWHv7SKyG9fXeZH/QjWhc7XR1PYEWoFr0Fng\nP28N8M9+3/24C3GqiDymqp+KyOv4dB/qeoN87o/L0SS/RfjzZpOWYHomm2+rnq/j9KxV1W3+XG1S\nYWOIlmjZBkBVHw5OIiK5qpqScU5R0zNpDiL0xQj93wXsCu8Dtvl95biBPIoL5XYD0+OFT/YPUFxa\nhKqQzTtF5LlQkc+AWnHdxMrEDSwaB5wlIr1wvWm+iD9vMm/sbNHS22p6JpFWpueeOBuj+FvfHn/e\nZDuHSOvZ0tAj+CNBD4P49dD2F4ERfnlIaHtuY8cm4w+4Btf9tMGgG4hltv134K64fW1xI3tHp8q2\nbNPS9DQ9o6xn1LXMFj2/USO11PXYqBWRASJysffGDTyXiHQGvgJ2ixsAc4uIBI1CNeJJdOw3Rery\nDC3G1UP2jy+jXmHcyM15IpIrIleIyBBV3aOqc1V1oT9fKnp/ZIWW/vNNz+Taanomz87Ia+nPmx16\n1mnVwhO4RHr/ghuhV4Ub6j1HVZeGwyMRKcU1+qwGZqrLCZR2ROQ2XP3ddepGS4b3CW7gy1e4vsQv\n4/KoV6fJtqzS0ttieibXZtMzefZGVktvQ/T1bE64QcNwKAc3ZPt9v16AG7V5A3UDSYKW9SNxE/8c\nED4+2SFRApsFONjbdAwub8lr+B4AcWW74Br4HgMGptiurNPS9DQ9o6xnVLXMVj1VtWVtEEAfoJNf\n/j6wA+jq108B7gLO3s/xKftyuJ4I0/1yif/fDpd98xq/PhmXQuHg+AtI3FB0Ut+dLbJamp6mZ5T1\nzDYto65nws9r4kW4zi/3BZ7AeeUFgcC4bIt/9MsH4FLhzgQOTXC+VN/Qx+Na+/t5W4N8M6NxOWuC\nvsNPAxfiG3ni7UrFhcg2LU1P0zPKekZZy2zUM+F3aMZFaI/zzP/mty/E9b3NwyUAexs41u87mgQ5\nf1L+ZereDObi6hcnAI+E9t/gv0M7XHe2RYTeLNJgX9ZoaXqanlHWM+paZpue+/wOTbwIT+EaRwCG\n4qaAvAuXF+Sq0AVZlNEvU9fo3hn4EjcI5l7ctKUAI4ENwE/8emkabcsqLU1P0zPKekZZy2zUc5/f\no4kXoRhXV9YDN3vSjX77ZNwk6KWkIetmMy/MDTjPPBo3g9oRuMlUHgEGx3/HNNiVdVqanqZnlPWM\nqpbZqmeiv/2Og1BV9f11twH/hZskowbIFzcxeykucVmh+qybvvtYxlDfF1hVb8TloukE/CduLoRq\nVT1fVd8Pldc02ZV1WoLpmWxMz+QRVS2Dz8o2PRPSTK+4DpcD/xrcrFm/zLSH24edwZvFROBDv9wu\nfn+GbcwKLU1P0zPKemaDltmkZwO7m3kRzgb+7peLQvvT2vWqmTa/ApwT2EkGegJku5amp+kZZT2j\nqmW26hn+a1KqDXXDwduo6lO4ySr+WVW/EJEcP+IvlRkYW4S3uQMuf/v/+m171V+VDNuVVVqCN+UX\nwwAAAKBJREFU6ZlsTM/kEVUtvR1Zp2eYJmdzjbsI6/22SH853PytK/1fZMhSLcH0TDamZ/KIpJaQ\ntXoCzU/3HdmLsA8WquprmTZiH2SblmB6JhvTM3lEWUvIPj2BZibrCyeQMr4ZpmVyMT2Ti+mZXLJV\nz2+czdUwDMP4dvKN5oMwDMMwvr2YgzAMwzASYg7CMAzDSIg5CMMwDCMh5iAMwzCMhPx/rS/EpGHw\nBAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11091fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "data = pd.read_csv('ch08/spx.csv', index_col = 0, parse_dates=True)\n",
    "spx = data['SPX']\n",
    "\n",
    "spx.plot(ax = ax, style = 'k-')\n",
    "\n",
    "crisis_data = [\n",
    "               (datetime(2007, 10, 11), 'Peak of bull market'),\n",
    "               (datetime(2008, 3, 12), 'Bear Stearns Fails'),\n",
    "               (datetime(2008, 9, 15), 'Lehman Bankruptcy')\n",
    "               ]\n",
    "\n",
    "for date, label in crisis_data:\n",
    "    ax.annotate(label, xy = (date, spx.asof(date) + 50),\n",
    "                xytext = (date, spx.asof(date) + 200),\n",
    "                arrowprops = dict(facecolor = 'black'),\n",
    "                horizontalalignment = 'left', verticalalignment = 'top')\n",
    "    \n",
    "ax.set_xlim(['1/1/2007', '1/1/2011'])\n",
    "ax.set_ylim([600, 1800])\n",
    "\n",
    "ax.set_title('Important dates in 2008-2009 financial crisis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Polygon at 0x111c606d8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hs0YTrNXBPRy5b9oUhiYqqce9Es2fH4axfvIT+Pzn4fbbNXwzGQr5hGTdWaMJ1uowOAiv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\nyazQAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bcfb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color = 'k', alpha = 0.3)\n",
    "circ = plt.Circle((0.7, 0.2), 0.15, color = 'b', alpha = 0.3)\n",
    "pgin = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]], color = 'g', alpha = 0.5)\n",
    "\n",
    "ax.add_patch(rect)\n",
    "ax.add_patch(circ)\n",
    "ax.add_patch(pgin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving plot to file"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "plt.savefig('figpath.svg')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "plt.savefig('figpath.png', dpi = 400, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from io import StringIO\n",
    "\n",
    "buffer = StringIO()\n",
    "plt.savefig(buffer)\n",
    "plot_data = buffer.getvalue()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## matplotlib configute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.rc('figure', figsize = (10, 10))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "font_options = {'family': 'monospace',\n",
    "                'weight': 'bold',\n",
    "                'size': 'small'}\n",
    "plt.rc('font', **font_options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting functions in pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas import Series\n",
    "from pandas import DataFrame\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = Series(np.random.randn(10).cumsum(), index = np.arange(0, 100, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110bc7e10>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SV6J6O+ss+POfHSQttYbhq47s+1JZtWw5hla991MZ9OwJX/taPHhb0oYxfNWR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\nIiKij/4C40eA4PuoG40AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bcd828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = DataFrame(np.random.randn(10, 4).cumsum(0),\n",
    "               columns = ['A', 'B', 'C', 'D'],\n",
    "               index = np.arange(0, 100, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111ca2400>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R8fLjTAAmpKTgp6Ag9PX2VvufmsiqLD1zBnuKinCgSxcGqosaODpibuvWeD4t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\niAf8H+rZidTebvekAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ca82b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r6l+TvDbr+384OIus3RuTfCBJuvubVfWtJNck+fK+zJBzsatm2YuvCN2YdHntuHZVdXWS\nzyS5rbu/eQBz5Mx2XL/u/q2Nx29mfR/Wn4qr88Yifzv/Icn1VXVxVf1G1jfcHtvnefLLFlm7Y0n+\nIEk29u+8Msl/7Oss2U7lzFf0d9Us41ew2o1Jl9Yia5fkfUkuS/LRjasgp7r72oObNT+34Pr9wo/s\n+yQ5owX/dh6vqs8leSzJ80mOdvfXD3DaZOHfvQ8mubuqHs36f8jf090/OLhZ83NV9akkK0kur6pv\nZ/1ffL4o59gsbjQKADBsT240CgBwIRNYAADDBBYAwDCBBQAwTGABAAwTWAAAwwQWAMAwgQUAMExg\nAQAME1gAAMMEFgDAMIEFADBMYAEADBNYAADDBBYAwDCBBQAwTGABAAwTWAAAwwQWAMAwgQUAMExg\nAQAME1gAAMMEFgDAMIEFADBMYAEADBNYAADDBBYAwDCBBQAwTGABAAwTWAAAwwQWAMAwgQUAMExg\nAQAM2zGwququqnquqh7bZsyHq+rxqnqkql43O0UAgOWyyBWsu5O85UxvVtWNSV7R3b+T5PYkHx+a\nGwDAUtoxsLr7C0l+uM2Qm5J8cmPsF5NcWlVXzkwPAGD5TOzBOpzkqU3HJzdeAwC4IB3azw+rqt7P\nzwMAOBfdXbv5uYnAOpnkZZuOr9p47bS6NdayWl1dzerq6kFPg12wdsvN+i0367e8qnbVVkkW/4qw\nNh6nc2+SP96YyHVJftTdz+16RgAAS27HK1hV9akkK0kur6pvJ3l/khcl6e4+2t2fraq3VtUTSX6S\n5J17OWEAgPPdjoHV3bcuMOaOmelwPltZWTnoKbBL1m65Wb/lZv0uTLWfe6Kqqu3BAgCWQVXtepO7\n/1UOAMAwgQUAMExgAQAME1gAAMMEFgDAMIEFADBMYAEADBNYAADDBBYAwDCBBQAwTGABAAwTWAAA\nwwQWAMAwgQUAMExgAQAME1gAAMMEFgDAMIEFADBMYAEADBNYAADDBBYAwDCBBQAwTGABAAwTWAAA\nwwQWAMAwgQUAMExgAQAME1gAAMMEFgDAMIEFADBMYAEADBNYAADDBBYAwDCBBQAwTGABAAwTWAAA\nwwQWAMAwgQUAMExgAQAME1gAAMMWCqyqOlJVx6vqRFXdeZr3L6+q+6rqkar6alW9Y3ymAABLorp7\n+wFVFyU5keSGJM8keTjJzd19fNOY9yf59e5+b1VdkeQbSa7s7p9tOVfv9HkAAOeDqkp3125+dpEr\nWNcmeby7n+zuU0nuSXLTljHfSXLJxvNLknx/a1wBAFwoDi0w5nCSpzYdP5316NrsE0k+X1XPJHlJ\nkj+amR4AwPKZ2uT+3iSPdvdLk/xukr+tqpcMnRsAYKkscgXrZJKrNx1ftfHaZm9M8oEk6e5vVtW3\nklyT5MtbT7a6uvrC85WVlaysrJzVhAEA9sLa2lrW1tZGzrXIJveLs75p/YYkzyb5UpJbuvvYpjF/\nk+Q/u/svq+rKrIfVa7v7B1vOZZM7ALAUzmWT+45XsLr7+aq6I8n9Wf9K8a7uPlZVt6+/3UeTfDDJ\n3VX1aJJK8p6tcQUAcKHY8QrW6Ie5ggUALIm9vk0DAABnQWABAAwTWAAAwwQWAMAwgQUAMExgAQAM\nE1gAAMMEFgDAMIEFADBMYAEADBNYAADDBBYAwDCBBQAwTGABAAwTWAAAwwQWAMAwgQUAMExgAQAM\nE1gAAMMEFgDAMIEFADBMYAEADBNYAADDBBYAwDCBBQAwTGABAAwTWAAAwwQWAMAwgQUAMExgAQAM\nE1gAAMMEFgDAMIEFADBMYAEADBNYAADDBBYAwDCBBQAwTGABAAwTWAAAwwQWAMAwgQUAMExgAQAM\nWyiwqupIVR2vqhNVdecZxqxU1Veq6mtV9cDsNAEAlkd19/YDqi5KciLJDUmeSfJwkpu7+/imMZcm\n+bckf9jdJ6vqiu7+3mnO1Tt9HgDA+aCq0t21m59d5ArWtUke7+4nu/tUknuS3LRlzK1JPtPdJ5Pk\ndHEFAHChWCSwDid5atPx0xuvbfbKJJdV1QNV9XBV3TY1QQCAZXNo8DyvT/LmJC9O8mBVPdjdTwyd\nHwBgaSwSWCeTXL3p+KqN1zZ7Osn3uvunSX5aVf+a5LVJfimwVldXX3i+srKSlZWVs5sxAMAeWFtb\ny9ra2si5FtnkfnGSb2R9k/uzSb6U5JbuPrZpzDVJ/neSI0n+W5IvJvmj7v76lnPZ5A4ALIVz2eS+\n4xWs7n6+qu5Icn/W92zd1d3Hqur29bf7aHcfr6rPJXksyfNJjm6NKwCAC8WOV7BGP8wVLABgSez1\nbRoAADgLAgsAYJjAAgAYJrAAAIYJLACAYQILAGCYwAIAGCawAACGCSwAgGECCwBgmMACABgmsAAA\nhgksAIBhAgsAYJjAAgAYJrAAAIYJLACAYQILAGCYwAIAGCawAACGCSwAgGECCwBgmMACABgmsAAA\nhgksAIBhAgsAYJjAAgAYJrAAAIYJLACAYQILAGCYwAIAGCawAACGCSwAgGECCwBgmMACABgmsAAA\nhgksAIBhAgsAYJjAAgAYJrAAAIYJLACAYQILAGDYQoFVVUeq6nhVnaiqO7cZ94aqOlVVb5+bIgDA\nctkxsKrqoiQfSfKWJK9KcktVXXOGcX+V5HPTkwQAWCaLXMG6Nsnj3f1kd59Kck+Sm04z7t1JPp3k\nu4PzAwBYOosE1uEkT206fnrjtRdU1UuTvK27P5ak5qYHALB8Dg2d50NJNu/NOmNkra6uvvB8ZWUl\nKysrQ1MAANi9tbW1rK2tjZyrunv7AVXXJVnt7iMbx3+RpLv7rzeN+Y+fP01yRZKfJPmT7r53y7l6\np88DADgfVFW6e1ffzC0SWBcn+UaSG5I8m+RLSW7p7mNnGH93kn/s7r8/zXsCCwBYCucSWDt+Rdjd\nz1fVHUnuz/qerbu6+1hV3b7+dh/d+iO7mQgAwK+KHa9gjX6YK1gAwJI4lytY7uQOADBMYAEADBNY\nAADDBBYAwDCBBQAwTGABAAwTWAAAwwQWAMAwgQUAMExgAQAME1gAAMMEFgDAMIEFADBMYAEADBNY\nAADDBBYAwDCBBQAwTGABAAwTWAAAwwQWAMAwgQUAMExgAQAME1gAAMMEFgDAMIEFADBMYAEADBNY\nAADDBBYAwDCBBQAwTGABAAwTWAAAwwQWAMAwgQUAMExgAQAME1gAAMMEFgDAMIEFADBMYAEADBNY\nAADDBBYAwDCBBQAwbKHAqqojVXW8qk5U1Z2nef/Wqnp04/GFqnr1/FQBAJZDdff2A6ouSnIiyQ1J\nnknycJKbu/v4pjHXJTnW3T+uqiNJVrv7utOcq3f6PACA80FVpbtrNz+7yBWsa5M83t1PdvepJPck\nuWnzgO5+qLt/vHH4UJLDu5kMAMCvgkUC63CSpzYdP53tA+pdSe47l0kBACyzQ5Mnq6o3JXlnkusn\nzwsAsEwWCayTSa7edHzVxmu/oKpek+RokiPd/cMznWx1dfWF5ysrK1lZWVlwqgAAe2dtbS1ra2sj\n51pkk/vFSb6R9U3uzyb5UpJbuvvYpjFXJ/l8ktu6+6FtzmWTOwCwFM5lk/uOV7C6+/mquiPJ/Vnf\ns3VXdx+rqtvX3+6jSd6X5LIkH62qSnKqu6/dzYQAAJbdjlewRj/MFSwAYEns9W0aAAA4CwILAGCY\nwAIAGCawAACGCSwAgGECCwBgmMACABgmsAAAhgksAIBhAgsAYJjAAgAYJrAAAIYJLACAYQILAGCY\nwAIAGCawAACGCSwAgGECCwBgmMACABgmsAAAhgksAIBhAgsAYJjAAgAYJrAAAIYJLACAYQILAGCY\nwAIAGCawAACGCSwAgGECCwBgmMACABgmsAAAhgksAIBhAgsAYJjAAgAYJrAAAIYJLACAYQILAGCY\nwAIAGCawAACGCSwAgGECCwBg2EKBVVVHqup4VZ2oqjvPMObDVfV4VT1SVa+bnSYAwPLYMbCq6qIk\nH0nyliSvSnJLVV2zZcyNSV7R3b+T5PYkH9+DuXLA1tbWDnoK7JK1W27Wb7lZvwvTIlewrk3yeHc/\n2d2nktyT5KYtY25K8skk6e4vJrm0qq4cnSkHzh+J5WXtlpv1W27W78K0SGAdTvLUpuOnN17bbszJ\n04wBALgg2OQOADCsunv7AVXXJVnt7iMbx3+RpLv7rzeN+XiSB7r77zaOjyf579393JZzbf9hAADn\nke6u3fzcoQXGPJzkt6vq5UmeTXJzklu2jLk3yZ8l+buNIPvR1rg6l0kCACyTHQOru5+vqjuS3J/1\nrxTv6u5jVXX7+tt9tLs/W1VvraonkvwkyTv3dtoAAOevHb8iBADg7OzJJnc3Jl1eO61dVd1aVY9u\nPL5QVa8+iHlyeov87m2Me0NVnaqqt+/n/Njegn87V6rqK1X1tap6YL/nyOkt8Lfz8qq6b+O/eV+t\nqnccwDQ5jaq6q6qeq6rHthlz9s3S3aOPrEfbE0lenuTXkjyS5JotY25M8s8bz38/yUPT8/DYs7W7\nLsmlG8+PWLvz57HI+m0a9/kk/5Tk7Qc9b4/F1y/JpUn+PcnhjeMrDnreHguv3fuTfPDn65bk+0kO\nHfTcPTpJrk/yuiSPneH9XTXLXlzBcmPS5bXj2nX3Q939443Dh+J+Z+eTRX73kuTdST6d5Lv7OTl2\ntMj63ZrkM919Mkm6+3v7PEdOb5G1+06SSzaeX5Lk+939s32cI2fQ3V9I8sNthuyqWfYisNyYdHkt\nsnabvSvJfXs6I87GjutXVS9N8rbu/lgS/6r3/LLI798rk1xWVQ9U1cNVddu+zY7tLLJ2n0jyqqp6\nJsmjSf58n+bGudtVsyxymwb4JVX1pqz/a9HrD3ounJUPJdm8P0RkLZdDSV6f5M1JXpzkwap6sLuf\nONhpsYD3Jnm0u99UVa9I8i9V9Zru/q+Dnhh7Yy8C62SSqzcdX7Xx2tYxL9thDPtvkbVLVb0mydEk\nR7p7u8uq7K9F1u/3ktxTVZX1fSA3VtWp7r53n+bImS2yfk8n+V53/zTJT6vqX5O8Nuv7fzg4i6zd\nG5N8IEm6+5tV9a0k1yT58r7MkHOxq2bZi68IX7gxaVW9KOs3Jt36x/veJH+cvHCn+NPemJR9t+Pa\nVdXVST6T5Lbu/uYBzJEz23H9uvu3Nh6/mfV9WH8qrs4bi/zt/Ick11fVxVX1G1nfcHtsn+fJL1tk\n7Y4l+YMk2di/88ok/7Gvs2Q7lTNf0d9Vs4xfwWo3Jl1ai6xdkvcluSzJRzeugpzq7msPbtb83ILr\n9ws/su+T5IwW/Nt5vKo+l+SxJM8nOdrdXz/AaZOFf/c+mOTuqno06/8hf093/+DgZs3PVdWnkqwk\nubyqvp31f/H5opxjs7jRKADAsD250SgAwIVMYAEADBNYAADDBBYAwDCBBQAwTGABAAwTWAAAwwQW\nAMCw/w+VRQEhgbv5DQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d28240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = Series(np.random.rand(16), index = list('abcdefghijklmnop'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111cd8da0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.plot(kind = 'bar', ax = axes[0], color = 'k', alpha = 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111cd8da0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.plot(kind = 'barh', ax = axes[0], color = 'k', alpha = 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = DataFrame(np.random.rand(6, 4),\n",
    "               index = ['one', 'two', 'three', 'four', 'five', 'sex'],\n",
    "               columns = pd.Index(['A', 'B', 'C', 'D'], name = 'Genus'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11001a390>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AQCcBBQDQSUABAHQSUAAAnQQUAEAnAQUA0ElAAQB0ElAAAJ0EFABAJwEFANBJQAEAdBJQ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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bf8a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11003fdd8>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E2K9U1Q0zj79i/qMDAKx8BzxN2clpSmi03WlKgEOxlKcpAQBYQmIMAKCRGAMAaCTGAAAa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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff63d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111d4de48>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VNfnafpP//M0kR2+z/MCqekZr7ZEdfdj4+NjWx6Ojg4yODvZwXACA4RsfH8/4+PhQtrWn\nMfbYNo//ebv3/3Tyn09L8uPW2qodvL+SnNBa+/lUPmwwGNvD8QAAZt5gMMhgMNj6fN26ddPe1u6+\nM/ZQkoMmH9euVnxKa+2hJPdU1elPLauqF00+vCbJO7dZ/mtTHxUAYN+zyxhrrT2YLd8NuyVbvsD/\nCy/v5HGSnJXkLVV1U1XdluS1k8vfmeRfVtXNk8vfOv3RAQDmv91epmytvWkny9+xzeMV2732vSSv\n2sF7fpTkd/d8TACAfZN7ggEAdCTGAAA6EmMAAB2JMQCAjsQYAEBHYgwAoCMxBgDQkRgDAOhIjAEA\ndCTGAAA6EmMAAB2JMQCAjsQYAEBHYgwAoCMxBgDQkRgDAOhIjAEAdCTGAAA6EmMAAB0t7j3ArkxM\njO1w+eGHLMvEholZnWUhOvCwwzM2MdF7jK6WLVmSifXre48xb4wsXdp7BIB5p1prvWfYoapqc3U2\nAIBtVVVaazWd97pMCQDQkRgDAOhIjAEAdCTGAAA6EmMAAB2JMQCAjsQYAEBHYgwAoCMxBgDQkRgD\nAOhIjAEAdCTGAAA6EmMAAB2JMQCAjsQYAEBHYgwAoCMxBgDQkRgDAOhIjAEAdCTGAAA6EmMAAB2J\nMQCAjsQYAEBHYgwAoCMxBgDQkRgDAOhIjAEAdCTGAAA6EmMAAB2JMQCAjsQYAEBHYgwAoCMxBgDQ\nkRgDAOhIjAEAdCTGAAA6EmMAAB2JMQCAjsQYAEBHYgwAoCMxBgDQkRgDAOhIjAEAdCTGAAA6Wtx7\ngF0555yx3iNA7v7BdVlx9PLeY7CP2nzL3Vk5suKXll+3aVOWH3dch4lg7hlZujQXXXBB7zFmzJyO\nsdHRsd4jQDbetjKnrF3dewz2URuv2Zixk075peUr77orq889t8NEMPdMrF/fe4QZ5TIlAEBHYgwA\noCMxBgDQkRgDAOhIjAEAdCTGAAA6EmMAAB2JMQCAjsQYAEBHYgwAoCMxBgDQkRgDAOhIjAEAdCTG\nAAA6EmMAAB2JMQCAjsQYAEBHex1jVbWkqs4bxjAAAAvNMM6MPSfJHw1hOwAAC84wYuyDSVZU1d9X\n1WVV9ZokqarPV9X/Pvn496rqTycfv6eqbq2qW6rqnUP4fACAeWsYMfb+JN9tra1KcnWSl08uPzzJ\nMZOPX57ka1W1KsmbkxyfZHWSP6iqXxvCDAAA89LiIW/v60neVVVHJ7k9ybOr6rBsCa+3J3lLks+3\n1h5Nkqr6XLaE2s072tj4+NjWx6Ojg4yODoY8LgDAnhsfH8/4+PhQtjXUGGutfb+qnp3klUmuTXJw\nkn+d5KHW2k+rao+2NxiMDXM8AIChGAwGGQwGW5+vW7du2tsaxmXKh5IctM3z/5Lk3Um+lmRjkvdm\nyxmzTP5zbVU9vaqemeT127wGALDg7PWZsdbag1X1jaq6JclV2RJXv9Vau7uq7s2W/9vya5Pr3lhV\nn0hyfZKW5GOttR1eogQAWAiGcpmytfam7RZdNrn8ifziWbO01i5JcskwPhcAYL5zB34AgI7EGABA\nR2IMAKAjMQYA0JEYAwDoSIwBAHQkxgAAOhJjAAAdiTEAgI7EGABAR2IMAKAjMQYA0JEYAwDoSIwB\nAHQkxgAAOhJjAAAdiTEAgI7EGABAR2IMAKCjxb0H2JWJibHeI0AOP2RZJjZM9B6DfdSBhx2esYmJ\nX1q+bMmSTKxfP/sDwRw0snRp7xFmVLXWes+wQ1XV5upsAADbqqq01mo673WZEgCgIzEGANCRGAMA\n6EiMAQB0JMYAADoSYwAAHYkxAICOxBgAQEdiDACgIzEGANCRGAMA6EiMAQB0JMYAgBkxOjqaqtqn\n/oyOjg7931O11oa+0WGoqjZXZwMAdq+qsq/9Xb6zn2lyeU1nm86MAQB0JMYAADoSYwAAHYkxAGBO\nuP/++3PWWWfl+c9/fo4//visWbMmV155Ze+xZpwYAwDmhLVr12YwGOSuu+7K9ddfn0996lP5p3/6\np95jzTgxBgB095WvfCUHHHBA/uAP/mDrsuc973k5//zz8+STT+Z973tfTjjhhKxcuTJ//dd/nSS5\n9tprc/LJJ+eMM87I0UcfnbPPPnvre4866qg8+OCDSZIbbrghJ5988tb3/Pqv/3pWrVqVF7/4xfnp\nT386iz/lji3uPQAAwD/8wz9k1apVO3zt4x//eJ797GfnW9/6Vh5//PGsWbMmp556apLkpptuyu23\n357DDjssa9asyTe/+c2ceOKJqfrFu0w89fwv/uIv8uEPfzirV6/OI488kqc//ekz+4NNgRgDAOac\nt73tbdm4cWP233//HHnkkbn11lvzmc98Jknyk5/8JHfeeWf222+/vOQlL8ny5cuTJCtXrszExERO\nPPHEnd7fbM2aNXn3u9+ds846K294wxtyxBFHzNrPtDMuUwIA3R177LG54YYbtj7/0Ic+lK985SvZ\ntGlTkuTSSy/NjTfemBtvvDHf/e5385u/+ZtJkgMOOGDrexYtWpQnnngiSbJ48eI8+eSTSZJHH310\n6zp//Md/nI9//OP52c9+ljVr1uQf//EfZ/xn2x0xBgB0d8opp+Sxxx7LRz/60a3LHn744VRVXvnK\nV+bDH/7w1tC6884788gjj+xye0cdddTWuPvsZz+7dfndd9+dY489Nu973/ty/PHH5zvf+c4M/DR7\nxmVKAGBO2LBhQ971rnfl4osvzq/8yq/kmc98Zi6++OKcfvrpueeee7Jq1aq01nLooYdmw4YNv/T+\nbb8nduGFF+Ytb3lLlixZksFgsHX5JZdckq9+9atZtGhRjj322LzqVa+ajR9tl/xuSgBgRvjdlFPj\nMiUAQEdiDACgIzEGANCRGAMA6EiMAQB0JMYAADoSYwAAHYkxAICOxBgAsKANBoMcfPDB+fnPf97l\n8/06JABg1lx44SW5997NM7b9kZFn56KL3jXl9b/3ve/l29/+dkZGRvKFL3whv/M7vzNjs+2MGAMA\nZs29927O6OjYjG1/YmLPtn355Zfnt37rt3LCCSfkE5/4RJcYc5kSAFiwLr/88rzxjW/MGWeckauv\nvjqbNm2a9RnEGACwIG3cuDH33XdfXvva1+YFL3hBjj322FxxxRWzPocYAwAWpMsvvzynnnpqDjzw\nwCTJGWeckU9+8pOzPofvjAEAC86jjz6av/3bv82TTz6Z5cuXJ0kef/zxbN68Obfeemte+MIXztos\nzowBAAvO5z//+SxevDh33HFHbr755tx8882544478vKXv3zWz46JMQBgwbn88svz+7//+zniiCNy\n6KGHbv1z/vnn54orrsiTTz45a7NUa23WPmxPVFWbq7MBALtXVdn+7/K5dp+xPbWjn2mb5TWtbc7V\n4Kmq9uY3f6D3GJAkufsH12XF0ct7j8Ee2nzL3Vk5smK36123aVOWH3fctD9nZOnSXHTBBdN+P+yr\ndhYu89lMxNic/gL/TN4UDvbExttW5pS1q3uPwR7aeM3GjJ10ym7XW3nXXVl97rnT/pyJ9eun/V4A\n3xkDAOhIjAEAdCTGAAA6EmMAAB2JMQCAjsQYAEBHYgwAoCMxBgAsSKOjo3nGM56RZz3rWTnkkENy\n2mmn5b777pv1Oeb0TV8BgH3LhR+8MPf+8N4Z2/7IspFc9D9dNKV1qypf+tKXcvLJJ+fxxx/Peeed\nl7e//e353Oc+N2Pz7YgYAwBmzb0/vDeja0dnbPsTGyb2aP2nfrXR/vvvn9NPPz3vfve7Z2CqXXOZ\nEgBY8B555JF8+tOfzurVs/+r75wZAwAWrLVr12bx4sV5+OGHc+ihh+bqq6+e9RmcGQMAFqwrr7wy\nDz74YB577LFceumlOemkk3L//ffP6gxiDABYsJ76zlhV5fWvf30WLVqUjRs3zuoMYgwAIFvOkm3e\nvDlHH330rH6u74wBAAvWaaedlkWLFqWqcuSRR+byyy8XYwDAvmtk2cge335iT7c/Vffcc8+MzbEn\nxBgAMGumekPWhcR3xgAAOhpKjFXVe6rq1qq6pareWVVHVtXtVfWxqrqtqr5cVQdMrruiqq6qquur\n6tqq+tVhzAAAMB/tdYxV1aokb05yfJLVSf7HJM9J8oIkl7bWjkvy/yb5ncm3fCzJ21prxye5IMlH\n9nYGAID5ahjfGXtZks+31h5Nkqr6XJKXJ7m7tXbr5Do3JBmtqmcmOTHJZ6qqJl/bb2cbHh8f2/p4\ndHSQ0dHBEMYFANg74+PjGR8fH8q2ZuIL/E9F1mPbLPvnJE/PljNxP26trZrKhgaDseFOBgAwBIPB\nIIPBYOvzdevWTXtbw/jO2NeTrK2qp0+e+Vqb5Gv5/6Nsq9baQ0nuqarTn1pWVS8awgwAAPPSXsdY\na+3GJJ9Icn2S65L8dZLNSdpO3vKmJG+pqpuq6rYkr93bGQAA5quhXKZsrV2S5JLtFr9om9f/YpvH\nE0leNYzPBQCY79xnDABYsK644oocf/zxOeigg3LEEUfkt3/7t/ONb3xjVmdwB34AYNZccuGF2Xzv\nvTO2/WePjORdF03tLv9/+Zd/mYsvvjgf/ehHc+qpp2b//ffP1Vdfnb/7u7/LmjVrZmzG7YkxAGDW\nbL733oyNjs7Y9scmJqa03k9+8pN84AMfyCc/+cm87nWv27r81a9+dV796lfP0HQ75jIlALDgXHfd\ndXnssceydu3a3qOIMQBg4fnRj36UpUuX5mlP659C/ScAAJhlhxxySB544IE8+eSTvUcRYwDAwrN6\n9eoccMAB2bBhQ+9RxBgAsPA861nPyrp163L++efnyiuvzM9+9rM88cQT+fKXv5z3v//9szqL/5sS\nAFiQ3vOe92T58uX5sz/7s7zpTW/KQQcdlBe/+MX5kz/5k1mdQ4wBALPm2SMjU779xHS3vyfOPPPM\nnHnmmTM0zdSIMQBg1kz1hqwLie+MAQB0JMYAADoSYwAAHYkxAICOxBgAQEdiDACgIzEGANCRGAMA\n6GhO3/R1YmKs9wiQJDn8kGWZ2DDRewz20IGHHT6lO30vW7IkE+vXT/tzRpYunfZ7gX5GR0dz//33\nZ7/99suiRYtyzDHH5Oyzz84f/uEfpqpmbY5qrc3ah+2JqmpzdTYAYPeqKtv/XX7hn/957n3ggRn7\nzJGlS3PRBRdMad2jjjoql112WU4++eQ89NBDufbaa/OOd7wjg8Egl1122Q7fs6OfaZvl0yq4OX1m\nDADYt9z7wAMZPffcGdv+np7lfiqsDjrooLzmNa/JsmXL8tKXvjTvfe97c8wxx8zEiL/Ed8YAACYd\nf/zxee5zn5uvf/3rs/aZYgwAYBuHH354HnzwwVn7PDEGALCN++67LwcffPCsfZ4YAwCYdP311+f7\n3/9+Xvayl83aZ4oxAGDBe+ihh/LFL34xZ555Zs4+++wce+yxs/bZ/m9KAGDBOu2007J48eI87WlP\nyzHHHJP3vve9eetb3zqrM4gxAGDWjCxdulc3WZ7K9qfqnnvumbE59oSbvgIAM2JnN0idz2bipq++\nMwYA0JEYAwDoSIwBAHQkxgAAOhJjAAAdiTEAgI7cZwwAmBFHHnlkqqZ1t4c568gjjxz6Nt1nDABg\nL7nPGHPK+Ph47xHYC/bf/Gb/zV/23cIlxhg6/0GZ3+y/+c3+m7/su4VLjAEAdCTGAAA6mtNf4O89\nAwDAVE33C/xzNsYAABYClykBADoSYwAAHXWNsar6V1X1nar6x6r6452s879V1Z1VdVNVrZztGdm5\n3e2/qnpFVW2uqr+f/PO/9JiTX1ZVH6+qH1bVLbtYx7E3R+1u/zn25q6qem5VfaWq/qGqbq2qd+xk\nPcffHDSV/Ted46/br0Oqqqcl+VCS30jy/STXV9WVrbXvbLPOq5L8i9baC6rqhCTrk7y0y8D8gqns\nv0lfa629dtYHZHf+jySXJrl8Ry869ua8Xe6/SY69uemJJO9prd1UVQcmuaGqrvF337yx2/03aY+O\nv55nxl6S5M7W2vdaaz9P8qkkr9tunddl8j82rbVvJVlSVctmd0x2Yir7L0n2rV9Kto9orW1M8uNd\nrOLYm8OmsP8Sx96c1Fr77621myYfP5zkjiRHbLea42+OmuL+S/bw+OsZY0ck+W/bPP+n/PIPtP06\n9+1gHfp3EdxWAAABqklEQVSYyv5LktWTp9m/VFXHzM5oDIFjb/5z7M1xVTWaZGWSb233kuNvHtjF\n/kv28PjrdpmSBeGGJCOttUcmT7tvSPKrnWeChcCxN8dNXuL6T0neOXmGhXlkN/tvj4+/nmfG7ksy\nss3z504u236d5+1mHfrY7f5rrT3cWntk8vFVSfarqoNnb0T2gmNvHnPszW1VtThb/iL/m9balTtY\nxfE3h+1u/03n+OsZY9cneX5VHVlV+yf53SRf2G6dLyT5N0lSVS9Nsrm19sPZHZOd2O3+2/Y7DlX1\nkmy5yfCDszsmu1DZ+fcaHHtz3073n2Nvzrssye2ttb/ayeuOv7ltl/tvOsdft8uUrbV/rqq3Jbkm\nW6Lw4621O6rqrVtebh9rrf1fVfXqqroryU+T/F6veflFU9l/SU6vqvOS/DzJz5K8sd/EbKuqrkgy\nSHJIVd2b5ANJ9o9jb17Y3f6LY2/Oqqo1Sc5KcmtV3ZikJfmfkxwZx9+cN5X9l2kcf34dEgBAR+7A\nDwDQkRgDAOhIjAEAdCTGAAA6EmMAAB2JMQCAjsQYAEBHYgwAoKP/D1MueetGLM94AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd52c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'barh', stacked = True, alpha = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tips = pd.read_csv('ch08/tips.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>1708</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0  1708\n",
       "day        \n",
       "Fri      19\n",
       "Sat      87\n",
       "Sun      76\n",
       "Thur     62"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_counts = pd.crosstab(tips.day, tips.size)\n",
    "party_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [Fri, Sat, Sun, Thur]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_counts = party_counts.ix[:, 2:5]\n",
    "party_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td> 0.888889</td>\n",
       "      <td> 0.055556</td>\n",
       "      <td> 0.055556</td>\n",
       "      <td> 0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td> 0.623529</td>\n",
       "      <td> 0.211765</td>\n",
       "      <td> 0.152941</td>\n",
       "      <td> 0.011765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td> 0.520000</td>\n",
       "      <td> 0.200000</td>\n",
       "      <td> 0.240000</td>\n",
       "      <td> 0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td> 0.827586</td>\n",
       "      <td> 0.068966</td>\n",
       "      <td> 0.086207</td>\n",
       "      <td> 0.017241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "size         2         3         4         5\n",
       "day                                         \n",
       "Fri   0.888889  0.055556  0.055556  0.000000\n",
       "Sat   0.623529  0.211765  0.152941  0.011765\n",
       "Sun   0.520000  0.200000  0.240000  0.040000\n",
       "Thur  0.827586  0.068966  0.086207  0.017241\n",
       "\n",
       "[4 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_pcts = party_counts.div(party_counts.sum(1).astype(float), axis = 0)\n",
    "party_pcts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fab064c0f50>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mmWcwb948XHjhhe7+ZumD8ePH4x//+Ad+/vOf4+2330ZsbGyXdZ566ikMHToUS5Yswd69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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab064f3a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_pcts.plot(kind = 'bar', stacked = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms and Density Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tips['tip_pct'] = tips['tip']/tips['total_bill']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fda7f28>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NTPaBXGpyqcllTiY1udQ2Of47c6jKzOdFxC9GxHvj+G/jFhHfFxEvjYhrEfFURBxFxHe2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\n6MBQBQDQgaEKAKADQxUAQAf/P9qNr/E571JHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fdb1c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['tip_pct'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110026978>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0KDFeBICiCBq63P1uSSeHPAYQWpm3i5BYSA8ARcGO9MAwyrxdhESnCwCKgtAF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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11363c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "tips['tip_pct'].plot(kind = 'kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "comp1 = np.random.normal(0, 1, size = 200)\n",
    "comp2 = np.random.normal(10, 2, size = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "values = Series(np.concatenate([comp1, comp2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1138dddd8>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1xVLKfXEtfF2qtT7d4XEBAJbGLKHrCxHx+n3Hq3u3zaTW+uLev18qpXw0rr1ceWjounDh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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f72a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values.hist(bins = 100, alpha = 0.3, color = 'k', normed = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1152a36a0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WvuVhViAQ/DbloMHBgwc1fvx4ph0AgAd8e9OBMeZkSbdJGiipv6SuxpivuU0FuOfXwta1\na1fFx8erqqrKdRQACJ0Ej1+/VNKJR32eXv/YsccMaOSYcyR9YK3dJUnGmJclTZT058ZONHPmzE9/\nnZ2drezs7PYlB3xo7969qqurU7du3VxHaVRKSorKysp8mw8AXMjJyVFOTk67XsPTjXONMfGSCiSd\nJ2mbpA8lXWWtXXPUMZdI+q61dooxZoKkh621E4wxoyQ9J+l0SdWSnpK01Fr7+0bOw8a5iAmFhYW6\n+OKLtWHDBtdRGnX22Wfr5z//uSZNmuQ6CgD4Vls2zvV0hc1aW2uMuUXSmzpy+fUJa+0aY8xNR75s\nZ1tr5xljLjHGrJe0T9L19c9dYYx5VtJHkmolfSxptpd5Ab/r16+fnnrqKdcxjislJUXbtm1zHQMA\nQofRVAAi5r777tMpp5yi6dOnu44CAL7VlhU2ChsAAEAUMUsUAAAghChsAAAAPkdhAwAA8DkKGxAg\n1157rYqLi5s/EAAQKhQ2IEBee+01derUyXWMJi1cuFC1tbWuYwBAqHCXKBAQNTU1SkxMVHV1teLi\n/PuzVr9+/bRixQqlpKS4jgIAvsRdokCIVVRUqG/fvr4ua9J/xlMBACLH33/zA/hUWVmZ+vXr5zpG\nsyhsABB5FDYgIMrLyylsABCjPJ0lCiByJkyYoEGDBrmO0SzmiQJA5FHYgIDo3bu3evfu7TpGs0aP\nHq3KykrXMRAFDe+rBOA97hIFALTa/v37lZGRoXPPPVePPPKI+vTp4zoSEBjcJQoAiLg9e/Z87rEu\nXbpow4YNSk5O1oQJE7RhwwYHyYDYQWEDABzXH/7wB02dOlWNXcXo0qWLHn74Yd1222264IILVF5e\n7iAhEBu4JAoAaNSHH36oqVOnavHixTr55JObPPYnP/mJVq5cqTlz5kQpHRBcbbkkSmEDAuLcc8/V\nnDlzlJSU5DoKYkBVVZVGjx6tX/ziF5o+fXqzx9fW1qqsrExpaWlRSAcEG4UNCKmgjKVq8P7772vU\nqFHq1q2b6yhoo9tvv10VFRV65plnXEcBQoebDoCQqqioUHJyciDKmnTkH/u8vDzXMdBGu3bt0p/+\n9Cf94he/cB0FQD32YQMCoLy8PFDD1Jl2EGy9evVSQUGBunfv7joKgHrB+HEdiHFBmSPagMIWfJQ1\nwF8obEAABGWOaIPU1FQKW4y74oortHTpUtcxgNCgsAEBMG3aNN19992uY7QYK2w466yzdP/997uO\nAYQGhQ0IgN69e2vgwIGuY7RYVlaWBg8e7DoGHLr++uu1YMECbdmyxXUUIBTY1gMAoH379umWW27R\nE088EbG7kW+99VYlJSXp3nvvjcjrAWHBth4AgDb585//rF27dkV065jvfOc7+uMf/6iampqIvSYQ\nqyhsAADNmjVL3/nOdyL6mkOHDtXFF1+s9evXR/R1gVjEJVEAiHGrV6/WhRdeqKKiIsXHx7uOA4Qe\nl0SBEKqpqdHIkSPFDyXwyp///GddeeWVlDXAxyhsgM9VVFRox44dMqZVP4w59+9//1slJSWuY6AF\n5s6dq6uvvtp1DABNoLABPhe0TXMb/P73v9eCBQtcx0ALLFq0SKNHj3YdA0ATmCUK+FzQ5og2SElJ\nUXl5uesYaIHExETXEQA0gxU2wOeCNke0Qb9+/Zh2gE/NmTNHL730kusYQGBR2ACfC+olUVbYcDRr\nrWbNmuU6BhBYXBIFfO473/mODh8+7DpGq7HChqNdeOGFuvbaa7Vz50717t3bdRwgcFhhA3yuR48e\n6tOnj+sYrTZkyBBNmDDBdQw0Yc6cOVH7YaBLly46//zz9eqrr0blfEDYsHEuAMSggoICnXfeeSou\nLo7aljHPPfec/vrXv2ru3LlROR/gV2ycCwBokTlz5ujSSy+N6v5+U6ZMUU5Ojg4ePBi1cwJhQWED\ngBg0d+5cXXrppVE9Z8+ePbVixQp17NgxqucFwoBLogAQY3bt2qVBgwZp+/btlCfAAS6JAiGzdetW\njR8/3nUMhMzbb7+ts88+m7IGBAiFDfCxbdu26dChQ65jtNnChQuVn5/vOgaOcdJJJ2nGjBmuYwBo\nBfZhA3wsqGOpGrz88svq27evsrKyXEfBUcaNG+c6AoBWYoUN8LGgjqVqwLQDNMZaq9LSUtcxgECh\nsAE+FtSxVA2YdoDGVFRUaPjw4aqtrXUdBQgMChvgY0EvbCkpKRQ2fE6/fv2UmpqqFStWuI4CBAbv\nYQN87N5773UdoV24JIrjyc7OVk5OjsaOHes6ChAIrLABPta1a1d17drVdYw2S09P15e+9CXXMVDv\no48+0s033+w6hiRp8uTJevfdd13HAAKDwgbAMz179tT999/vOgbqvfvuu4qPj3cdQ5J0zjnn6P33\n3+d9bEALUdgAIEYsWLBAkyZNch1DktS3b19NmTJFFRUVrqMAgcBoKgCIAbW1terTp4/Wrl0b6BtZ\ngDBgNBUAoFF5eXnq168fZQ0IKAob4FO5ubnKzs52HQMh8f777/vmciiA1mNbD8CnysrK1KlTJ9cx\n2m3JkiU6dOiQzjrrLNdRYtq3v/1tVVVVuY4BoI1YYQN8qqysLNBzRBssWrRIf/vb31zHiHkJCQnq\n2bOn6xgA2ojCBvhU0KccNGA8FZpSVFSkF154wXUMwPcobIBPhaWwMe0ATdm/f79+9KMfuY4B+B6F\nDfCp8vLyUFwSZZ4ompKZmak9e/bwZwRoBoUN8Klnn31W06dPdx2j3Vhhc2/btm2uIxxXXFyczjjj\nDC1cuNB1FMDXKGyAT51wwgnq0KGD6xjt1qNHD910001ic2s3tm/frlNPPVV1dXWuoxzXxIkTKWxA\nMyhsADxljNEDDzwgY1q1qTciZMmSJRo/frzi4vz71z2FDWief/8PBgC02+LFizVhwgTXMZp0+umn\n68Ybb3QdA/A1ChsAhFgQCltiYqKuu+461zEAX2P4O+BDdXV1MsZwGRHtUltbq169emnjxo3q3bu3\n6zgA6jH8HQiJ+fPn69JLL3UdAwFXUVGhc845h7IGhACFDfCh8vLyUP0ju3z5cr3yyiuuY8Sc1NRU\nzZ0713UMABFAYQN8qKysLBRTDhqsX79ef/rTn1zHAIDAorABPhSWsVQNmCeKlrj77rv1zjvvuI4B\n+BKFDfChsBU2ph2gJaqrq7VgwQLXMQBforABPrRz585QzBFtwAobWuL000/X0qVLXccAfIltPQAf\nstbKWuvr3elbw1qrxMREVVRUqGvXrq7jxITFixere/fuOvXUU11HabGSkhKNGTNGFRUVbGmDUGNb\nDyAkjDGhKWvSkd/PfffdxzzRKPrNb36jZcuWuY7RKmlpaUpISFBRUZHrKIDvtOhfBGPMy8aYKcaY\n8PwLAiCq/ud//kdJSUmuY8SMjz76SOPGjXMdo1WMMVwWBY6jRZdEjTHnS7pe0gRJf5P0lLW2wONs\nLcYlUQD4j927d+vEE0/Unj17FB8f7zpOq5SUlKhXr17q0qWL6yiAZzy7JGqtfctae7WksZI2S3rL\nGLPQGHO9MeaE1kcFAHglNzdXo0ePDlxZk6T09HTKGtCIFl/iNMb0lnSdpG9K+ljSIzpS4P7lSTIg\nRtXU1Ojw4cOuYyDAli1bFrjLoQCa1tL3sP1D0vuSukj6krX2Umvti9ba/yeJW76ACHr++ed1ww03\nuI6BAMvKytJXvvIV1zEARFBCC4973Fo77+gHjDEdrbXV1lp+jAMiqKysLFR7sDUoKCjQggULdOON\nN7qOEnpTp051HQFAhLX0kui9jTy2KJJBABwRtikHDXbt2qUnn3zSdQwEBG8LAD6rycJmjEkxxpwm\nqbMxZowxZmz9R7aOXB4FEGFhLWxMO0BLvf3225oyZYrrGICvNHdJ9CIdudEgXdJDRz1eJelOjzIB\nMS3shc1ayy72aNKpp56qjz76iD8rwFGaLGzW2mckPWOM+Yq19u9RygTEtP3794fyPWyJiYnq0KGD\nKisr1b17d9dx4GOpqamKj49XaWmp0tPTXccBfKG5S6LX1P9ykDFmxrEfUcgHxJzFixdr+PDhrmN4\ngsui3psxY4Y++eQT1zHaxRijMWPG6OOPP3YdBfCN5m46SKz/b1dJSY18AECL3X333erZs6frGKG1\na9cu/fGPfwzFCDAKG/BZzV0Sfaz+v3e39QTGmIslPawj5fAJa+0vGjnmUUlflLRP0nXW2uX1j3eX\n9EdJwyXVSfpva+2StmYB4NbXvvY11xFCLTc3V6NGjVJcXPDHPo8dO1b/+hf7sgMNWrpx7i+NMd2M\nMScYY942xmw/6nJpU8+Lk/Q7Hbl5IUvSVcaYoccc80VJGdbaIZJukjTrqC8/ImmetfZUSaMkrWnR\n7woAYtDy5cs1ZswY1zEiYvr06Zo9e7brGIBvtPTHsAuttZWSpurILNHBkm5vwfPGSyq01hZZaw9J\nekHStGOOmSbpWUmqXz3rbozpZ4zpJulsa+1T9V87XJ8BANCIFStWaPTo0a5jRAR3hwKf1dLC1nDp\ndIqkv1lrW/qO1jRJxUd9XlL/WFPHlNY/dpKkHcaYp4wxucaY2caYzi08LxBIe/fu1f79+13HQECt\nWLFCo0aNch0DgAdaWtheM8aslXSapLeNMcmSDnoXS9KRkjhW0u+ttWMl7Zf0A4/PCTj129/+Vvfc\nc4/rGAioX/3qV6G9wxiIdS2aJWqt/YEx5peSPrHW1hpj9unzlzYbUyrpxKM+T69/7NhjBhznmGJr\n7bL6X78k6Y7jnWjmzJmf/jo7O1vZ2dktiAf4S1lZmQYNGuQ6hmdKS0v1xz/+UXfddZfrKKF00UUX\nuY4AoBE5OTnKyclp12sYa23LDjRmoqRBOqrkWWufbeY58ZIKJJ0naZukDyVdZa1dc9Qxl0j6rrV2\nijFmgqSHrbUT6r+2QNK3rLXrjDF3Sepirf1caTPG2Jb+PgA/u/LKK3XppZeG9m7KLVu2aOLEiSop\nKXEdBQFQW1urvLw8LvMidIwxsta26o2aLVphM8b8SVKGpOWSausftqq/WeB46lfjbpH0pv6zrcca\nY8xNR75sZ1tr5xljLjHGrNeRbT2uP+olvifpeWPMCZI2HvM1IHTCOpaqQb9+/VRRUaG6urpQbD0B\nb9XW1uqMM87Qjh071KUL46sR21pU2CSNkzSsLctY1tr5kjKPeeyxYz6/5TjPXSHp9NaeEwiqsrKy\nUI6latCxY0clJiZq9+7d6t27t+s48LkOHTpo6NChysvL0/jx413HAZxq6Y+4eZLC+68I4BMJCQmh\nXmGTpJSUFJWXl7uOgYAYMWKEVq1a5ToG4FxLC1sfSauNMW8YY+Y2fHgZDIhFq1atUp8+fVzH8BTz\nRCNvy5YtmjJliusYnhgxYoRWrlzpOgbgXEsvic70MgSA2HHnnXdqyJAhrmOEyvLly1VXV+c6hidG\njhypefPmuY4BONfSbT0WGGMGShpirX3LGNNFUry30QCE0YUXXug6QuiEecPcUaNGqX///q5jAM61\ndJbot3RkH7SGmwXSJL3iVSgAQMstX748NCOpjtWvXz8999xzrmMAzrX0PWzflXSmpEpJstYWSurr\nVSgAQMuFeYUNwBEtLWzV1tqahk+MMQk6sg8bgAjZvn27KisrXcdAwFRWVmrbtm065ZRTXEcB4KGW\nFrYFxpg7JXU2xlwg6W+SXvUuFhB77r33Xj3xxBOuYyBgkpKStH79esXH87ZiIMxaWth+IGm7pFWS\nbpI0T9KPvQoFxKKwTzloUFVVpRtvvNF1jNAwxig1NdV1DAAea1Fhs9bW6chNBjdba6dbax9neCcQ\nWbFS2Dp37qynnnpKtbW1zR8M1Hv22Wd16NAh1zEAZ5osbOaImcaYHToyxL3AGLPdGPPT6MQDYkd5\neXmox1I1SEhIUM+ePbVjxw7XURAg9957r9atW+c6BuBMcytst+nI3aGnW2t7WWt7SfqCpDONMbd5\nng6IIWVlZTGxwiYx7QCtN3LkSEZUIaY1V9i+Lukqa+2mhgestRslXSPpG14GA2JJbW2tUlNT1atX\nL9dRoiIlJYXCFgG1tbWhnXBwrJEjRzKiCjGtucJ2grX2c9ctrLXbJZ3gTSQg9sTHxys/P19xcS29\nDyjYGAA0S2x5AAAgAElEQVQfGe+//77OP/981zGigsKGWNfcaKqaNn4NAI7r1ltvjZnVRC/l5+dr\n8ODBrmNEBUPgEeuaK2yjjDGN7eRpJHXyIA+AGDBu3DjXEUIhPz9fWVlZrmNExUknnaSrr75adXV1\nMbMSDRytyT/11tp4a223Rj6SrLVcEgUAh2KpsMXFxen++++nrCFm8ScfAALIWhtThQ2IdRQ2wAeK\nioq0e/du1zEQIHv27FHXrl1jYu8+ABQ2wBfuuOMOvf76665jIEB69uypTZs2yRjjOgqAKKCwAT4Q\nK1MOGlhrdfnllzNqqJ0oa0DsoLABPhBLUw6kI0Vj8eLFqqiocB0FAfPzn/+cPzeISRQ2wAdiZfD7\n0Zh2gLZ4++23tXz5ctcxgKijsAGOVVdXa+/evTG3kSzTDtAWWVlZys/Pdx0DiDoKG+BYVVWVJk+e\nHHP7SzEAvu3279+vzZs3u47hBIUNsSq2/oUAfKhPnz564403XMeIOlbY2m7hwoW67rrrXMdwgsKG\nWNXcaCoA8MQNN9zAXY5tFMsb5jYUNmstf34QUyhsAJwYMmSI6wiBlZ+fr9GjR7uO4USvXr302GOP\nqba2VgkJ/BOG2MElUQAImPz8fA0bNsx1DGeuuuoqyhpijrHWus7QbsYYG4bfBwA0x1qrnj17qrCw\nUMnJya7jAGgDY4ysta26ps8KG+BYXl6e9uzZ4zoGAqKqqkoTJkygrAExhsIGOHbLLbcoNzfXdQwE\nRLdu3TR//nzXMQBEGYUNcCwWpxw0+NrXvqbS0lLXMQDA9yhsgGOxNvj9aIWFhRQ2tMn111+v4uJi\n1zGAqKGwAQ41jKXq2bOn6yhOMO0AbbV161atXLnSdQwgaihsgEMVFRVKTk6OubFUDZh2gLZi4gFi\nTWz+KwH4RE1NjS666CLXMZxhha11du/erbfeest1DF/IyspSXl6e6xhA1FDYAIcyMjL05JNPuo7h\nTEpKCoWtFZYsWaIHHnjAdQxfGD58OCtsiClsFQ3AmenTp2v//v2uYwRGLM8QPdawYcO0du1a1dXV\nxexbChBbKGwAnElNTXUdIVDy8/M1YcIE1zF8ISkpSR988IHrGEDU8GMJAAQEK2yfNXr0aFbXEDOY\nJQoAAWCtVbdu3bRly5aY3QYGCAtmiQIB8/7776uqqsp1DATA/v37dcMNN1DWgBjFChvg0EknnaS3\n3npLGRkZrqMAAKKEFTYgQKy1Kisri/k33n/rW9/SRx995DoGAPgahQ1w5JNPPlGHDh3UpUsX11Gc\n2rFjh4qKilzHQECdddZZWrdunesYgOcobIAj27Zti/nVNenItAPGU6GtevTowQa6iAkUNsCRbdu2\nKSUlxXUM55h2gPbIysrS6tWrXccAPEdhAxzp3LlzTM8RbUBha15ZWZlmzZrlOoYvDRs2jBU2xAQK\nG+DIGWecoR/+8IeuYzjHAPjmLVu2TK+88orrGL7EChtiBaOpADh17rnnasyYMa5j+BoTDo5v6NCh\nKiwsVG1treLj413HATzDChsAp7p3765Bgwa5juFrFLbj69q1q7Zv305ZQ+hR2ADA5yhsTYv1rXEQ\nG5h0AAA+Vltbq6SkJJWXlyspKcl1HAARwKQDIEBeeukl1dbWuo4Bnzt06JAeeughyhoQ41hhAxw4\nePCgunfvroMHD8qYVv2QBQAIOFbYgIAoLy9Xv379KGv1vv/97+vVV191HQMBVllZqbq6OtcxAM9Q\n2AAHGEv1WdXV1dq8ebPrGAiwUaNGaePGja5jAJ6hsAEOlJWVMZbqKCkpKdq2bZvrGAiwYcOGsYEu\nQo3CBjjACttn9e/fn8KGdsnKymJEFUKNwgY4MGDAAE2ePNl1DN/o37+/tm7d6jqG7xQVFen22293\nHSMQmCmKsGM0FeDA1KlTXUfwlbS0NJWWlrqO4Tsff/yx1qxZ4zpGIGRlZenRRx91HQPwDCtsAJzL\nzMzUG2+84TqG7zDhoOVOPfVU1dTUiC2eEFYUNgDOdejQQWlpaa5j+A6FreW6du2qvLw8tspBaFHY\nAMCn8vLyKGwAJDHpAAB86fDhw0pKStLOnTsZbg6EDJMOgACoqqrSX/7yF9cx4HPWWr3++uuUNQCS\nWGEDom7lypW6+uqrtWrVKtdRAAAOsMIGBACb5jZu9uzZ+vGPf+w6BgJuyZIlqq2tdR0DiDgKGxBl\n27ZtYyxVIxITE5kFiXb76le/ylxahBKFDYgyVtgax7QDREJWVhYzRRFKFDYgyhj83rj+/fsz7QDt\nxogqhBWFDYiycePGacKECa5j+E7DChs3EEkFBQW6/PLLXccIJIbAI6wobECUff3rX9cZZ5zhOobv\nJCUlKS4uTpWVla6jOLdq1SqKaxtxSRRhRWED4BvFxcXq1q2b6xjOMZKq7U499VRlZGS4jgFEHIUN\ngG/06NGDWZBiJFV7dOvWTX/9619dxwAijsIGAD7DChuAYzHpAAB8pKamRt26ddOePXvUqVMn13EA\neIBJB4DPFRYW6pVXXnEdAz52wgknaNOmTZQ1AJ9BYQOiaOHChfr73//uOoavxfpquTGGjZUBfA6F\nDYii0tJSpaWluY7hWx9++KHOPvts1zEQcNZaPfXUU6qrq3MdBYgYChsQRVu3blX//v1dx/Ctvn37\nasuWLa5jIOCMMfrpT3+qoqIi11GAiKGwAVFUWlpKYWtCamqqysrKWBlBu7GBLsKGwgZE0datW7kk\n2oSOHTuqW7du2rFjh+soTtTV1am2ttZ1jFBgpijCxvPCZoy52Biz1hizzhhzx3GOedQYU2iMWW6M\nGX3M1+KMMbnGmLleZwW8duWVV2rw4MGuY/haWlpazA6Bz8vL02mnneY6Riiwwoaw8bSwGWPiJP1O\n0kWSsiRdZYwZeswxX5SUYa0dIukmSbOOeZlbJfF/HULhtttuU3JysusYvta/f3+VlZW5juFEfn4+\nhT5CWGFD2CR4/PrjJRVaa4skyRjzgqRpktYedcw0Sc9KkrV2iTGmuzGmn7W23BiTLukSST+XNMPj\nrAB84NVXX1VCgtd/NfkTEw4iJysrS1OnTnUdA4gYry+JpkkqPurzkvrHmjqm9KhjfiPpdkmxvTET\nEENitaxJFLZI6tatm+666y7XMYCI8e1NB8aYKZLKrbXLJZn6DwAILQobgOPx+kfZUkknHvV5ev1j\nxx4zoJFjpku61BhziaTOkpKMMc9aa7/R2Ilmzpz56a+zs7OVnZ3d3uwAEDWHDh1SZWWlhgwZ4joK\ngAjLyclRTk5Ou17D0+Hvxph4SQWSzpO0TdKHkq6y1q456phLJH3XWjvFGDNB0sPW2gnHvM45kr5v\nrb30OOdh+Dt8b968eerevbvOPPNM11HgU9ZaGcPFBCDsfDf83VpbK+kWSW9Kypf0grV2jTHmJmPM\njfXHzJO0yRizXtJjkm72MhPgyt///netWbOm+QOh/fv3u47gBGUNwPF4/h42a+18a22mtXaItfaB\n+sces9bOPuqYW6y1g621o6y1uY28xoLjra4BQcGUg5ax1qpPnz7au3ev6ygIuNraWt1+++1MzkAo\n+PamAyBsGPzeMsYYpaenq6SkxHUUBFx8fLz+/Oc/M58WoUBhA6KEwe8tN2DAABUXFzd/INAMJh4g\nLChsQBQcOHBAe/fuVZ8+fVxHCYT09PSYKmw1NTXasGGD6xihxMQDhAWFDYiCuro6PfTQQ7ypvIVi\nbYVt5cqV+vKXv+w6RiixwoawoLABUZCYmKjvfve7rmMExqBBg1RVVeU6RtTk5eVp+PDhrmOEEits\nCIvYnQEDwLe++c1vuo4QVfn5+RQ2j4wcOVLf//73XccA2o0VNgBwjBU27yQlJemKK65wHQNoNwob\nADjGChuA5lDYAMChmpoaDR48WAMHDnQdBYCPUdiAKPjlL3+pzZs3u44BH+rQoYPeeecdxcXx1zGA\n4+NvCCAKZs+erZqaGtcxAmX37t0xdacoADSFwgZ4zFrLlIM2uO222/TXv/7VdQyEQE1NjaZNmyZr\nresoQJtR2ACP7dy5Ux07dlTXrl1dRwmUAQMGME8UEdGhQwctXbo0pjZjRvhQ2ACPlZSUKD093XWM\nwIm18VTw1rBhw5h4gECjsAEeKy4u1oABA1zHCJxYWGGrrKzUG2+84TpGTMjKymLiAQKNwgZ4LCsr\nS//7v//rOkbgxMI80dzcXP3sZz9zHSMmsMKGoKOwAR47+eSTde6557qOETgDBgxQ586dXcfwFBMO\nooeZogg6ChsAX+rRo4eWLVvmOoan8vPzlZWV5TpGTDjttNP02GOPuY4BtBmFDQAcYYUterp06aJR\no0a5jgG0mQnDvjTGGBuG3weA2GGtVa9evbRu3TolJye7jgMgiowxstaa1jyHFTYAcKC6ulpf//rX\nKWsAWoTCBnho165duvnmm13HgA916tRJjz76qOsYAAKCwgZ4aNOmTVq0aJHrGIG1d+9erVu3znUM\nAHCOwgZ4qLi4mCkH7bBy5Up94xvfcB0DIbFv3z4NGzaMmaIIJAob4KGSkhKmHLTDwIEDtXnzZtcx\nEBKJiYnatWuXSktLXUcBWo3CBniIsVTtk5qaqj179ujAgQOuoyAk2EAXQUVhAzzEJdH2iYuLU3p6\nurZs2eI6SkSVlpZq1qxZrmPEpKysLEZUIZAobICHbr31Vp133nmuYwTaoEGDVFRU5DpGRC1evFjz\n5s1zHSMmMQQeQUVhAzz0hS98Qf3793cdI9DOPvvs0L1JfOXKley67wiFDUHFpAMAiLLLL79cX/va\n1/TVr37VdZSYc+jQIR06dEhdunRxHQUxjEkHABAAK1as0MiRI13HiEknnHACZQ2BxAobAERRZWWl\nUlNTVVlZqfj4eNdxADjAChsA+Jy1Vo899hhlDUCrsMIGeOTFF19UZWWlvvWtb7mOAgDwEVbYAB9Z\nunSpdu/e7TpGKCxcuFCffPKJ6xgIkaqqKtcRgFahsAEe2bx5swYOHOg6Rij88Ic/VG5urusYCIna\n2lqlpKRo3759rqMALUZhAzxSVFSkQYMGuY4RCswURSTFx8crMzOT/dgQKBQ2wCOssEVOGKcdwK2R\nI0dq5cqVrmMALUZhAzywb98+VVVVqW/fvq6jhEJYVthycnL04IMPuo4BUdgQPBQ2wAMdOnTQggUL\nFBfH/2KRMGjQoFAUtvfee087d+50HQOisCF4+NcE8MAJJ5ygL3zhC65jhMbQoUM1YsQI1zHajRmi\n/jFy5Ejt37/fdQygxdiHDQCi5JRTTtErr7yiYcOGuY4CwKG27MNGYQOAKNi3b5+Sk5NVWVmphIQE\n13EAOMTGuQDgU3l5eRo6dChlDUCbsMIGAFFQVVWloqIiDR8+3HUUAI6xwgb4RHZ2NncD4jOSkpIo\nawDajMIGRNjBgwe1aNEi9ejRw3WUUCkvL9drr73mOgZCZvHixaqpqXEdA2gWhQ2IsOLiYqWnpys+\nPt51lFCpqKjQ//3f/7mOgZC54YYbtGbNGtcxgGZR2IAIYySVN04++WRt2rRJdXV1rqMgREaOHKnl\ny5e7jgE0i8IGRBhD372RmJioHj16aOvWra6jIERGjx5NYUMgUNiACGOFzTsZGRnauHGj6xit9oMf\n/EBPPvmk6xhoxJgxY/Txxx+7jgE0iw2BgAibMWMGl+08kpGRoQ0bNmjSpEmuo7TK4sWLdd5557mO\ngUaMGTNGy5cvl7VWxrRqlwUgqihsQIT16tXLdYTQuvzyy9WtWzfXMVqlrq5Oy5cv15gxY1xHQSOS\nk5N12WWXqaqqKnB/thBb2DgXADy0YcMGTZ48WVu2bHEdBYBPsHEuAPhMbm4uq2sA2o3CBgAeys/P\n19ixY13HABBwXBIFAA9Za3Xo0CF16NDBdRQAPsElUcCxJ598kt348RnGGMoagHajsAERVFBQoO7d\nu7uOEWpvvvmm/v3vf7uOgZB5/fXXlZ+f7zoGcFwUNiCCNm7cqIyMDNcxQi03N1dz5sxxHQMh869/\n/Uuvvvqq6xjAcVHYgAjauHGjTj75ZNcxQu2UU05RQUGB6xgImYYNdAG/orABEWKt1YYNG1hh81hm\nZqbWrVvnOkaLFBcXM/UiIBhRBb+jsAERsnv3bllrmXTgscGDB2vz5s06dOiQ6yhNstbqtNNOY1h9\nQAwdOlQlJSXau3ev6yhAoyhsQIT06tVLW7duZR6hxzp27Kj+/ftr06ZNrqM0qaioSAkJCUpLS3Md\nBS2QkJCgrKwsrVixwnUUoFEUNiCCEhMTXUeICffdd5+SkpJcx2jShx9+qPHjx1PgA+QHP/iB+vbt\n6zoG0Cg2zgUAD9x+++3q0aOHfvSjH7mOAsBn2DgXAHyiYYUNACKBwgYAHujatavGjRvnOgaAkOCS\nKBAhNTU1jCACADSLS6KAIzU1NerevbsOHz7sOgoAIIQobEAEbN68WampqUpISHAdJWb86le/Um5u\nrusYCJlly5bp3nvvdR0D+BwKGxABhYWFGjJkiOsYMWX9+vVatGiR6xgImc6dO+vpp592HQP4HAob\nEAGFhYU65ZRTXMeIKcOHD1deXp7rGAiZoUOHqry8XLt373YdBfgMChsQAevWraOwRdnw4cOVn5/v\nOsbnrF+/Xjk5Oa5joI3i4+M1duxYLVu2zHUU4DMobEAE7Ny5k8IWZVlZWcrLy5Pf7hB/6aWXNGfO\nHNcx0A7jxo2jsMF3KGxABLz44ou66KKLXMeIKX379lVCQoK2bdvmOspnfPDBBzrzzDNdx0A7nH76\n6Vq6dKnrGMBnsA8bgMB6//33NXbsWN/McK2rq1NycrJWrVql/v37u46DNtqzZ4/Ky8uVmZnpOgpC\nqi37sFHYACBC1qxZo0suuUSbNm1yHQWAj7FxLgA4tHDhQi6HAvAEu3wCQIRkZmYqIyPDdQwAIcQl\nUaCdiouLlZycrE6dOrmOAgAIAC6JAg5cffXV7LgPhJC11nfbxiB2UdiAdmLTXLd+/OMf6+WXX3Yd\nAyE0ceJErV692nUMQBKFDWiXyspKVVVVsYWDQ126dGGFE54YPHgwf7bgG54XNmPMxcaYtcaYdcaY\nO45zzKPGmEJjzHJjzOj6x9KNMe8YY/KNMauMMd/zOivQWuvWrdOQIUNkTKveioAIGjt2rHJzc13H\nQAidccYZFDb4hqeFzRgTJ+l3ki6SlCXpKmPM0GOO+aKkDGvtEEk3SZpV/6XDkmZYa7MknSHpu8c+\nF3BtzZo1OvXUU13HiGljxoxRbm6u0/caVVVV6YorruD9TiFDYYOfeL3CNl5SobW2yFp7SNILkqYd\nc8w0Sc9KkrV2iaTuxph+1toya+3y+sf3SlojKc3jvECrHDp0SBMnTnQdI6b169dPiYmJ2rx5s7MM\n77//vioqKlhpDZkRI0aouLhYu3fvdh0F8HwftjRJxUd9XqIjJa6pY0rrHytveMAYM0jSaElLvAgJ\ntNV///d/u44A/eey6EknneTk/O+++64mT57s5NzwTkJCgs444wytXr2aDZHhnO83zjXGdJX0kqRb\n61faAOAznnnmGXXr1s3Z+d9991395je/cXZ+eGf+/PmKi+P+PLjndWErlXTiUZ+n1z927DEDGjvG\nGJOgI2XtT9baOU2daObMmZ/+Ojs7W9nZ2W3NDCBgevbs6ezce/bsUUFBgcaPP/biAcKAsoZIyMnJ\nUU5OTrtew9NJB8aYeEkFks6TtE3Sh5KustauOeqYSyR911o7xRgzQdLD1toJ9V97VtIOa+2MZs7D\npAMATsydO1ePPvqo3nrrLddRAAREWyYdeD6ayhhzsaRHdOQGhyestQ8YY26SZK21s+uP+Z2kiyXt\nk3SdtfZjY8yZkt6TtEqSrf+401o7v5FzUNgAOFFdXa0dO3YoLY17ogC0jC8LWzRQ2ODCypUr1bNn\nTw0YMKD5gwEAqMcsUSCK7rvvPi1YsMB1DNSz1rL9Ajyxb98+rVixwnUMxDgKG9BGbJrrL4sXL9YF\nF1zgOgZCqKioSJdffrnrGIhxXBIF2qC2tlZJSUnavn27EhMTXceBpIMHD6p3797avn27unTp4joO\nQsRaq759+yo3N5e3QCAiuCQKRMmmTZvUt29fypqPdOrUScOHD9eyZcuicr7Dhw+roqIiKueCW8YY\nTZo0Se+9957rKIhhFDagDfLz8zVs2DDXMXCMM844QwsXLozKuZYsWaILL7wwKueCexQ2uEZhA9og\nMTFR06dPdx0Dx5g0aVLUbgSZP3++Lr744qicC+5R2OAahQ1og/PPP585oj50zjnnqKamRtF4T+u8\nefMobDFk5MiRmjhxog4fPuw6CmIUNx0AQCsVFRVp3Lhx2rZtmxISfD+SGYDPcNMBAETBP/7xD116\n6aWUNQBRQ2EDgFaKi4vT1Vdf7ToGgBjCJVEAAIAo4pIoEAVz585Vfn6+6xgAgBhCYQNa6eGHH1ZJ\nSYnrGGjCpk2b9I9//MN1DITQxx9/rMcff9x1DMQgChvQCtZarVy5UiNHjnQdBU3Yu3evZsyYEZXt\nPRBbjDH69a9/7ToGYhCFDWiFsrIyGWOUkpLiOgqaMHz4cNXV1Wn16tWuoyBkRo4cqZ07d6q4uNh1\nFMQYChvQCitWrNCIESNkTKveK4ooM8ZoypQpeu211yL6ujfeeKNKS0sj+poIlri4OJ177rl6++23\nXUdBjKGwAa2Qm5ursWPHuo6BFrj88sv10ksvRez1CgoK9Oqrr6pfv34Re00E0/nnn6+33nrLdQzE\nGAob0AoTJ07UlVde6ToGWmDy5MkqLi7WunXrIvJ6TzzxhL7+9a+zWS4+LWy8RxLRxD5sAEJrwYIF\nGjFihHr16tWu1zlw4IBOPPFELV68WBkZGRFKhyB77733dNZZZykujnUPtF5b9mGjsAFAM5555hm9\n+OKLmjdvnusoAEKAjXMBwAOvvfaabr75ZtcxAMQwVtgAoBmHDx9WXFwcl78ARERbVth49ywANIMb\nDQC4xo+LQAt94xvf0J49e1zHQBvU1tZG7G5R4GgHDx50HQExgsIGtMCOHTs0d+5cdevWzXUUtMHm\nzZt15plnavfu3a6jIETWr1+v4cOHs70HooLCBrTARx99pDFjxvAepoDKyMjQZZddpl/84hctfg7/\nCKM5GRkZqq6u1tq1a11HQQzgXx+gBZYsWaLx48e7joF2mDlzpp544gmtWbOm2WO3bdumMWPGaO/e\nvVFIhqBqGIHGdi+IBgob0AKLFi3SxIkTXcdAO6SlpWnmzJm67rrrVF1dfdzjDh8+rGuuuUaXXXaZ\nunbtGsWECKJLLrlE//znP13HQAxgWw+gGXV1derdu7cKCgrUt29f13HQDtZafeUrX1F2dra+973v\nfe7rdXV1+uY3v6mtW7fqn//8p+Lj4x2kRJDs27dPKSkpKi0t5T2uaDG29QA88s4771DWQsAYo2ee\neUYnnHDC5762detWffvb31ZlZSVlDS2WmJio6dOna8OGDRozZozrOAgxVtgAQNLLL7+sDz/8UPfc\nc486dOjgOg6AEGOWKAAAgM8xSxQAACCEKGwAAAA+R2EDmlBbW+s6AgAAFDagKXfeeaceeugh1zEA\nBMCLL76opUuXuo6BkKKwAU145513NG7cONcxAATApk2b9OSTT7qOgZDiLlHgOPbs2aMBAwZox44d\n6tixo+s4AHxu/fr1OvPMM1VaWqqEBLY5xfFxlygQQe+9954mTJhAWQPQIoMHD9agQYP01ltvuY6C\nEKKwAcfx7rvvavLkya5jAAiQa665Rs8//7zrGAghChtwHCUlJTr33HNdxwAQIFdccYVeffVV7d27\n13UUhAzvYQMAIILWrFmjoUOHyphWvUUJMYTRVAAAAD7HTQcAAAAhRGEDAADwOQobAACAz1HYgGO8\n99572rBhg+sYAAJuy5Yt/F2CiKGwAcf40Y9+pMLCQtcxAATcyy+/rJ/85CeuYyAkuEsUOMquXbs0\naNAglZeXq3Pnzq7jAAiwXbt26eSTT9b69evVp08f13HgI9wlCrTTm2++qXPOOYeyBqDdevXqpUsv\nvVRPP/206ygIAQobcJRXXnlFU6dOdR0DQEjcdNNNmj17turq6lxHQcBR2IB6+/fv1/z58/XlL3/Z\ndRQAITFx4kR17dpV8+fPdx0FAZfgOgDgF7W1tZo1a5aSk5NdRwEQEsYY/eEPf1Dfvn1dR0HAcdMB\nAABAFHHTAQAAQAhR2AAAAHyOwgYAAOBzFDbEvOrqam65BxAVy5cvV2lpqesYCCAKG2Leb3/7W33/\n+993HQNADPjLX/6in//8565jIIC4SxQxzVqrYcOGafbs2Tr77LNdxwEQctu3b1dmZqY+/vhjDRw4\n0HUcOMJdokArLVq0SHV1dTrrrLNcRwEQA5KTk3XLLbfoxz/+sesoCBgKG2La448/rhtuuEHGtOoH\nHQBos9tvv11vvfWWcnNzXUdBgHBJFDGroqJCmZmZKiwsVJ8+fVzHARBDZs2apddff11z5sxxHQUO\ntOWSKIUNMWv58uV64403dMcdd7iOAiDGHD58WLt372YUXoyisAEAAPgcNx0AAACEEIUNAADA5yhs\nAAA4Vltbq5qaGtcx4GMUNsSUAwcOaO/eva5jAMBn3H333ezNhiZx0wFiyj333KPS0lI99thjrqMA\nwKe2b9+uUaNG6fnnn9fkyZNdx4HHuEsUaMLWrVs1cuRILVu2TIMGDXIdBwA+41//+peuvfZaLVu2\nTP3793cdBx7iLlGgCbfeequ+/e1vU9YA+NIFF1ygm2++WVdccYUOHTrkOg58hsKGmDB37lytWLGC\n94gA8LU777xTPXv21OOPP+46CnyGS6IIvQMHDigzM1PPPPMM7w0B4Hv79+9Xx44dFR8f7zoKPMJ7\n2IDjKCgoUGZmpusYAABQ2AAAAPyOmw4AAAghNtUFhQ0AAJ+79tprdc8994irSbGLS6IInVWrVmnn\nzjj4pNQAAAiVSURBVJ3Kzs52HQUAIqKsrExTp07VqaeeqlmzZikxMdF1JLQDl0QR8z7++GNdeOGF\nqqiocB0FACImJSVFCxYsUHx8vMaPH6/Vq1e7joQoo7AhNF5++WVdeOGF+v3vf6//+q//ch0HACIq\nMTFRTz/9tP73f/9X55xzjlauXOk6EqKIS6IIvOrqat1999167rnn9I9//EOnnXaa60gA4KlNmzZp\n4MCBiotj3SWI2nJJNMGrMEC0rFq1SmvWrNHSpUvVr18/13EAwHMnnXSS6wiIMlbYAAAIiVdeeUXJ\nycmaOHGijGnVAg6iyJc3HRhjLjbGrDXGrDPG3HGcYx41xhQaY5YbY0a35rmIHdZaHTx40HUMAPCt\nTz75RNdee63Gjh2rBx98UMXFxa4jIUI8LWzGmDhJv5N0kaQsSVcZY4Yec8wXJWVYa4dIuknSrJY+\nF8eXk5PjOkJEWGuVl5en++67T8OHD9fdd9/drtcLy/cl0vi+NI7vy+fxPWmcX74v1157rQoKCvTg\ngw+qoKBAo0eP1plnnqn9+/c7yeOX70sYeL3CNl5SobW2yFp7SNILkqYdc8w0Sc9KkrV2iaTuxph+\nLXwujiPo/5Ns3LhR11xzjVJTUzVt2jSV/P/27j+0qvOO4/j7g02UbGFEQxLQNU3I/PWHlJbKoJM4\nxNJVRmsZmw5xq0P6R9P6h/vD7o85/COsA4UyKE6XYTuUZRt2TaE4U1YYFbaVOW3qTNalKKa5jXWo\nmA00t/nuj/Mkvc25N/amvZ5zc74vuNx7znlO/PrlyZPnPuc8zxkZ4dChQ3R3d3+mn1vteakUz0tx\nnpc4z0lxacrLggUL2LBhA4cPHyaXy9Hd3U1dXV2s3MTEBGfPnmV8fLxisaQpL9Wu0pMOlgKF47Ej\nRB2x25VZ+inPdVUin89z6dIlrl69Ov26cuUK+Xyerq6uWPm6ujo6OzvZt28f7e3tCUTsnHPVr7a2\nls7OzqLHRkZG2LZtG8PDw9TX19PR0UFraytr1qxhz549sfK3bt3i+vXr1NfXs3DhQr9H7g5L4yzR\nqqwBly9fZseOHdOPDZl6b2pq4siRI7HyY2NjbN++vWj5o0ePFi2/devWWPnm5mZ6e3tj5cfHx1m3\nbl3R8sePH4+Vz+VybN68OVa+paWFvr6+ouU3bdo0vZ3P57l58yaNjY2cOnUqVn50dJT169ezePFi\nGhoaaGhoYMmSJaxatSpWdurf3blzZ9FjzjnnPru2tjYGBgaYnJwkl8sxPDzMxYsXqampKVr+3Llz\nbNy4kRs3bjA5OcmiRYuoqalh7dq1nDhxomj5np4e+vv7kTT9Wr16NQcPHoyVHxwcZNeuXdPlILo5\nf+XKlRw4cCBWfmhoiN27d8f2L1++vKLlV6xYwf79+2P7K62is0QlfRX4iZk9HLb3AGZmzxWUOQi8\nYWa9YXsQ6ATabnduwc/wKaLOOeecqxppW4ftLaBDUiuQA7YAW2eU6QOeAnpDB++amY1JuvIpzgXK\n/08755xzzlWTinbYzOwjSV3ASaIJDj1mdl7Sk9FhO2Rmr0l6RNK/gf8CT8x2biXjdc4555xLo3mx\ncK5zzjnn3HxWtQ8hk/QtSe9I+kjSfQX7WyX9T9Lp8HohyTjvtFJ5CceeDQsUn5f0UFIxJk3SXkkj\nBXXk4aRjSoovTl2cpAuSzkr6h6S/JR1PUiT1SBqT9HbBvgZJJyUNSfqjpC8lGWMSSuQl0+2KpGWS\n/iTpnKQBSc+E/ZmuL0Xy8nTYX3Z9qdoRNkkrgEngF8APzex02N8KvGpma5KMLymz5GUVcAx4AFgG\nvA58JYvP9JK0F7hhZvFpQRkSFqf+F7ABGCW653SLmQ0mGlgKSHoPuN/MriYdS5IkfQ0YB16aalMl\nPQf8x8x+Fjr5DWYWXwNiHiuRl0y3K5JagBYzOyPpi8DfidZOfYIM15dZ8vIdyqwvVTvCZmZDZvYu\nxZcByewkhFny8ijwGzPLm9kF4F2yva5dZutIAV+cujRRxe3j58XM3gRmdlofBV4Mn18EHrujQaVA\nibxAhtsVM/vAzM6Ez+PAeaLBgUzXlxJ5WRoOp+tZogm5JwwxvhG+Cbn4QsTv83GlyaKu8OzaX2Zt\niL5AqUWrHRjQL+ktSb4g4Cc1mdkYRH+MgKaE40kTb1cASfcA9wJ/AZq9vkQK8vLXsKus+pLqDpuk\nfklvF7wGwvs3ZzltFLjbzO4DdgPHwjDkvDHHvGTKbXL0AtBuZvcCHwCZvIThZvVgaEMeAZ7yL36z\nytxtFSV4uwKEv7e/B3aFEaWZ9SOT9aVIXsquL2l80sE0M9s4h3MmCEPVZnZa0jCwHDj9OYeXmLnk\nhWhE7csF28vCvnmpjBwdBl6tZCwp9j5wd8H2vK4T5TCzXHj/UNLLRJeP30w2qtQYk9Qc1stsAS4n\nHVAamNmHBZuZbFck3UXUKfm1mb0Sdme+vhTLy1zqS6pH2MowfR1YUmO4mRpJ7UAH8F5SgSWs8Pp4\nH7BFUq2kNqK8ZHL2W2g0pjwOvJNULAmbXthaUi3R4tTx55BljKS6qVF5SV8AHiK7dQSidmRmW/L9\n8Pl7wCszT8iIT+TF2xUAfgX808yeL9jn9aVIXuZSX6p5luhjwM+BRuAacMbMviHpcWAfcItotuSP\nzey15CK9s0rlJRx7FvgBMEE0LHsysUATJOklovsIJoELwJNT91hkTZhK/jwfL07904RDSlz4QvMy\n0aWbu4CjWc2LpGPAemAJMAbsBf4A/I5oxP4i8G0zu5ZUjEkokZevk+F2RdKDwJ+BAaLfHQN+RDQw\n8FsyWl9myct3KbO+VG2HzTnnnHMuK+bLJVHnnHPOuXnLO2zOOeeccynnHTbnnHPOuZTzDptzzjnn\nXMp5h80555xzLuW8w+acc845l3LeYXPOOeecSznvsDnnnHPOpdz/AWaMzY4ZqIZLAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138c2ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values.plot(kind = 'kde', style = 'k--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro = pd.read_csv('ch08/macrodata.csv')\n",
    "macro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trans_data = np.log(data).diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005849</td>\n",
       "      <td>0.014215</td>\n",
       "      <td>0.088193</td>\n",
       "      <td>-0.128617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006838</td>\n",
       "      <td>-0.008505</td>\n",
       "      <td>0.215321</td>\n",
       "      <td>0.038466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000681</td>\n",
       "      <td>-0.003565</td>\n",
       "      <td>0.125317</td>\n",
       "      <td>0.055060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005772</td>\n",
       "      <td>-0.002861</td>\n",
       "      <td>-0.212805</td>\n",
       "      <td>-0.074108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000338</td>\n",
       "      <td>0.004289</td>\n",
       "      <td>-0.266946</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cpi        m1  tbilrate     unemp\n",
       "1  0.005849  0.014215  0.088193 -0.128617\n",
       "2  0.006838 -0.008505  0.215321  0.038466\n",
       "3  0.000681 -0.003565  0.125317  0.055060\n",
       "4  0.005772 -0.002861 -0.212805 -0.074108\n",
       "5  0.000338  0.004289 -0.266946  0.000000"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1132f7898>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AgSKoAQAABIqgBgAAECiCGgAAQKAIagAAAIEiqAEAAASKoAYAABAoghoAAECgCGoAAACB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11599b9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(trans_data['m1'], trans_data['unemp'])\n",
    "plt.title('Change in log %s vs. log %s' %('m1', 'unemp'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1132fa630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115c99f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115ce8a58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115d24400>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x115d6d438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115dadbe0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115dfe320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115e32a20>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x115e81c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115ecc9b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115f11eb8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115f61048>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x115f9c2b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115fe6d68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x116022550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11606fcc0>]], dtype=object)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jnLDFxsZyxx3J5OZuJTNzSrvxatpxU3pUR6w7q+Nd0Qm+ypYNJMFaQasG9Csd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\n7DQuX5YYNmyhU37qZbLuCKvVyvr1hzh1aiTr1xficDjIz89yRtv7Uxj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YiOVgqDC7pGTg786ldKH166pHVrBOGAkI+dQ/SXftHEB02SpO8DF4H3ADMwT5blFpfju4AHgTTg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\nA3XEfzFiKekuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd34c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(trans_data, diagonal = 'kde', color = 'k', alpha = 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Maps: Visualization Haiti Earthquake Crisis Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('ch08/Haiti.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial</th>\n",
       "      <th>INCIDENT TITLE</th>\n",
       "      <th>INCIDENT DATE</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>APPROVED</th>\n",
       "      <th>VERIFIED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4052</td>\n",
       "      <td>* URGENT * Type O blood donations needed in #J...</td>\n",
       "      <td>05/07/2010 17:26</td>\n",
       "      <td>Jacmel, Haiti</td>\n",
       "      <td>Birthing Clinic in Jacmel #Haiti urgently need...</td>\n",
       "      <td>1. Urgences | Emergency, 3. Public Health,</td>\n",
       "      <td>18.233333</td>\n",
       "      <td>-72.533333</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4051</td>\n",
       "      <td>Food-Aid sent to Fondwa, Haiti</td>\n",
       "      <td>28/06/2010 23:06</td>\n",
       "      <td>fondwa</td>\n",
       "      <td>Please help food-aid.org deliver more food to ...</td>\n",
       "      <td>1. Urgences | Emergency, 2. Urgences logistiqu...</td>\n",
       "      <td>50.226029</td>\n",
       "      <td>5.729886</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4050</td>\n",
       "      <td>how haiti is right now and how it was during t...</td>\n",
       "      <td>24/06/2010 16:21</td>\n",
       "      <td>centrie</td>\n",
       "      <td>i feel so bad for you i know i am supposed to ...</td>\n",
       "      <td>2. Urgences logistiques | Vital Lines, 8. Autr...</td>\n",
       "      <td>22.278381</td>\n",
       "      <td>114.174287</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4049</td>\n",
       "      <td>Lost person</td>\n",
       "      <td>20/06/2010 21:59</td>\n",
       "      <td>Genoca</td>\n",
       "      <td>We are family members of Juan Antonio Zuniga O...</td>\n",
       "      <td>1. Urgences | Emergency,</td>\n",
       "      <td>44.407062</td>\n",
       "      <td>8.933989</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4042</td>\n",
       "      <td>Citi Soleil school</td>\n",
       "      <td>18/05/2010 16:26</td>\n",
       "      <td>Citi Soleil, Haiti</td>\n",
       "      <td>We are working with Haitian (NGO) -The Christi...</td>\n",
       "      <td>1. Urgences | Emergency,</td>\n",
       "      <td>18.571084</td>\n",
       "      <td>-72.334671</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial                                     INCIDENT TITLE  \\\n",
       "0    4052  * URGENT * Type O blood donations needed in #J...   \n",
       "1    4051                     Food-Aid sent to Fondwa, Haiti   \n",
       "2    4050  how haiti is right now and how it was during t...   \n",
       "3    4049                                        Lost person   \n",
       "4    4042                                 Citi Soleil school   \n",
       "\n",
       "      INCIDENT DATE            LOCATION  \\\n",
       "0  05/07/2010 17:26       Jacmel, Haiti   \n",
       "1  28/06/2010 23:06              fondwa   \n",
       "2  24/06/2010 16:21             centrie   \n",
       "3  20/06/2010 21:59              Genoca   \n",
       "4  18/05/2010 16:26  Citi Soleil, Haiti   \n",
       "\n",
       "                                         DESCRIPTION  \\\n",
       "0  Birthing Clinic in Jacmel #Haiti urgently need...   \n",
       "1  Please help food-aid.org deliver more food to ...   \n",
       "2  i feel so bad for you i know i am supposed to ...   \n",
       "3  We are family members of Juan Antonio Zuniga O...   \n",
       "4  We are working with Haitian (NGO) -The Christi...   \n",
       "\n",
       "                                            CATEGORY   LATITUDE   LONGITUDE  \\\n",
       "0        1. Urgences | Emergency, 3. Public Health,   18.233333  -72.533333   \n",
       "1  1. Urgences | Emergency, 2. Urgences logistiqu...  50.226029    5.729886   \n",
       "2  2. Urgences logistiques | Vital Lines, 8. Autr...  22.278381  114.174287   \n",
       "3                          1. Urgences | Emergency,   44.407062    8.933989   \n",
       "4                          1. Urgences | Emergency,   18.571084  -72.334671   \n",
       "\n",
       "  APPROVED VERIFIED  \n",
       "0      YES       NO  \n",
       "1       NO       NO  \n",
       "2       NO       NO  \n",
       "3       NO       NO  \n",
       "4      YES       NO  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCIDENT DATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05/07/2010 17:26</td>\n",
       "      <td>18.233333</td>\n",
       "      <td>-72.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28/06/2010 23:06</td>\n",
       "      <td>50.226029</td>\n",
       "      <td>5.729886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/06/2010 16:21</td>\n",
       "      <td>22.278381</td>\n",
       "      <td>114.174287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20/06/2010 21:59</td>\n",
       "      <td>44.407062</td>\n",
       "      <td>8.933989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18/05/2010 16:26</td>\n",
       "      <td>18.571084</td>\n",
       "      <td>-72.334671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26/04/2010 13:14</td>\n",
       "      <td>18.593707</td>\n",
       "      <td>-72.310079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26/04/2010 14:19</td>\n",
       "      <td>18.482800</td>\n",
       "      <td>-73.638800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26/04/2010 14:27</td>\n",
       "      <td>18.415000</td>\n",
       "      <td>-73.195000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15/03/2010 10:58</td>\n",
       "      <td>18.517443</td>\n",
       "      <td>-72.236841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15/03/2010 11:00</td>\n",
       "      <td>18.547790</td>\n",
       "      <td>-72.410010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      INCIDENT DATE   LATITUDE   LONGITUDE\n",
       "0  05/07/2010 17:26  18.233333  -72.533333\n",
       "1  28/06/2010 23:06  50.226029    5.729886\n",
       "2  24/06/2010 16:21  22.278381  114.174287\n",
       "3  20/06/2010 21:59  44.407062    8.933989\n",
       "4  18/05/2010 16:26  18.571084  -72.334671\n",
       "5  26/04/2010 13:14  18.593707  -72.310079\n",
       "6  26/04/2010 14:19  18.482800  -73.638800\n",
       "7  26/04/2010 14:27  18.415000  -73.195000\n",
       "8  15/03/2010 10:58  18.517443  -72.236841\n",
       "9  15/03/2010 11:00  18.547790  -72.410010"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['INCIDENT DATE', 'LATITUDE', 'LONGITUDE']][: 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          1. Urgences | Emergency, 3. Public Health, \n",
       "1    1. Urgences | Emergency, 2. Urgences logistiqu...\n",
       "2    2. Urgences logistiques | Vital Lines, 8. Autr...\n",
       "3                            1. Urgences | Emergency, \n",
       "4                            1. Urgences | Emergency, \n",
       "5                       5e. Communication lines down, \n",
       "Name: CATEGORY, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CATEGORY'][: 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3593.000000</td>\n",
       "      <td>3593.000000</td>\n",
       "      <td>3593.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2080.277484</td>\n",
       "      <td>18.611495</td>\n",
       "      <td>-72.322680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1171.100360</td>\n",
       "      <td>0.738572</td>\n",
       "      <td>3.650776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.041313</td>\n",
       "      <td>-74.452757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1074.000000</td>\n",
       "      <td>18.524070</td>\n",
       "      <td>-72.417500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2163.000000</td>\n",
       "      <td>18.539269</td>\n",
       "      <td>-72.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3088.000000</td>\n",
       "      <td>18.561820</td>\n",
       "      <td>-72.293570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4052.000000</td>\n",
       "      <td>50.226029</td>\n",
       "      <td>114.174287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Serial     LATITUDE    LONGITUDE\n",
       "count  3593.000000  3593.000000  3593.000000\n",
       "mean   2080.277484    18.611495   -72.322680\n",
       "std    1171.100360     0.738572     3.650776\n",
       "min       4.000000    18.041313   -74.452757\n",
       "25%    1074.000000    18.524070   -72.417500\n",
       "50%    2163.000000    18.539269   -72.335000\n",
       "75%    3088.000000    18.561820   -72.293570\n",
       "max    4052.000000    50.226029   114.174287"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data[(data.LATITUDE > 18) & (data.LONGITUDE < 20) &\n",
    "           (data.LONGITUDE > -75) & (data.LONGITUDE < -70)\n",
    "           & data.CATEGORY.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def to_cat_list(catstr):\n",
    "    stripped = (x.strip() for x in catstr.split(','))\n",
    "    return [x for x in stripped if x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_all_categories(cat_series):\n",
    "    cat_sets = (set(to_cat_list(x)) for x in cat_series)\n",
    "    return sorted(set.union(*cat_sets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_english(cat):\n",
    "    code, names = cat.split('.')\n",
    "    if '|' in names:\n",
    "        names = names.split(' | ')[1]\n",
    "    return code, names.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2', 'Vital Lines')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_english('2. Urgences logistiques | Vital Lines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_cats = get_all_categories(data.CATEGORY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "english_mapping = dict(get_english(x) for x in all_cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Food Shortage'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "english_mapping['2a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Earthquake and aftershocks'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "english_mapping['6c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_code(seq):\n",
    "    return [x.strip(',')[0] for x in seq if x]\n",
    "\n",
    "all_codes = get_code(all_cats)\n",
    "\n",
    "code_index = pd.Index(np.unique(all_codes))\n",
    "\n",
    "dummpy_frame = DataFrame(np.zeros((len(data), len(code_index))),\n",
    "                         index = data.index, columns = code_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     1    2    3    4    5    6\n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "5  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "6  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummpy_frame.ix[: 6, : 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for row, cat in zip(data.index, data.CATEGORY):\n",
    "    codes = get_code(to_cat_list(cat))\n",
    "    dummpy_frame.ix[row, codes] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.join(dummpy_frame.add_prefix('category_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial</th>\n",
       "      <th>INCIDENT TITLE</th>\n",
       "      <th>INCIDENT DATE</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4052</td>\n",
       "      <td>* URGENT * Type O blood donations needed in #J...</td>\n",
       "      <td>05/07/2010 17:26</td>\n",
       "      <td>Jacmel, Haiti</td>\n",
       "      <td>Birthing Clinic in Jacmel #Haiti urgently need...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4042</td>\n",
       "      <td>Citi Soleil school</td>\n",
       "      <td>18/05/2010 16:26</td>\n",
       "      <td>Citi Soleil, Haiti</td>\n",
       "      <td>We are working with Haitian (NGO) -The Christi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4041</td>\n",
       "      <td>Radio Commerce in Sarthe</td>\n",
       "      <td>26/04/2010 13:14</td>\n",
       "      <td>Radio Commerce Shelter, Sarthe</td>\n",
       "      <td>i'm Louinel from Sarthe. I'd to know what can ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial                                     INCIDENT TITLE  \\\n",
       "0    4052  * URGENT * Type O blood donations needed in #J...   \n",
       "4    4042                                 Citi Soleil school   \n",
       "5    4041                           Radio Commerce in Sarthe   \n",
       "\n",
       "      INCIDENT DATE                        LOCATION  \\\n",
       "0  05/07/2010 17:26                   Jacmel, Haiti   \n",
       "4  18/05/2010 16:26              Citi Soleil, Haiti   \n",
       "5  26/04/2010 13:14  Radio Commerce Shelter, Sarthe   \n",
       "\n",
       "                                         DESCRIPTION  \n",
       "0  Birthing Clinic in Jacmel #Haiti urgently need...  \n",
       "4  We are working with Haitian (NGO) -The Christi...  \n",
       "5  i'm Louinel from Sarthe. I'd to know what can ...  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[: 5, : 5]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from mpl_toolkits.basemap import Basemap \n",
    "import matplotlib.pyplot as plt\n",
    "def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25, lllon=-75, urlon=-71):\n",
    "    # create polar stereographic Basemap instance. \n",
    "    m = Basemap(ax=ax, projection='stere', \n",
    "                lon_0=(urlon + lllon) / 2, \n",
    "                lat_0=(urlat + lllat) / 2,\n",
    "                llcrnrlat=lllat, \n",
    "                urcrnrlat=urlat, \n",
    "                llcrnrlon=lllon, \n",
    "                urcrnrlon=urlon, \n",
    "                resolution='f')\n",
    "    # draw coastlines, state and country boundaries, edge of map. \n",
    "    m.drawcoastlines()\n",
    "    m.drawstates()\n",
    "    m.drawcountries()\n",
    "    return m"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
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 },
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