wealth_and_population/population_barchart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)\n",
    "'''\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = ['Rank', 'Country', 'UN_Continental_region', 'UN_Statistical_region', \n",
    "        'Population', 'Population2015', 'percent_change']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##  There is problem your numeric data contains bad data (strings) or numeric data are converted to str.\n",
    "##  So first try convert to float or int by astype:\n",
    "#pop_list['Population'] = pop_list['Population'].astype(float)\n",
    "##  But if it failed need to_numeric with parameter errors='coerce':\n",
    "#pop_list['Population'] = pd.to_numeric(pop_list['Population'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pop_list = pd.read_table('country.dat', names=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                      int64\n",
       "Country                  object\n",
       "UN_Continental_region    object\n",
       "UN_Statistical_region    object\n",
       "Population                int64\n",
       "Population2015            int64\n",
       "percent_change           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(type(pop_list))\n",
    "#pop_list.head()\n",
    "pop_list.dtypes\n",
    "#pop_list.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/npr1/astroconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: RuntimeWarning: Tried to set a label via parameter 'x' in func 'hist' but couldn't find such an argument. \n",
      "(This is a programming error, please report to the matplotlib list!)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "hist() missing 1 required positional argument: 'x'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-b77294b82800>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#pop_list['Population'].plot().hist('Population')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpop_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Population'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m#pop_list.plot.hist(alpha=0.5)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#pop_list.plot.hist('Population')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/npr1/astroconda/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1890\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1891\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1892\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1893\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1894\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: hist() missing 1 required positional argument: 'x'"
     ]
    }
   ],
   "source": [
    "#pop_list['Population'].plot().hist('Population')\n",
    "pop_list['Population'].plot().hist(alpha=0.5)\n",
    "#pop_list.plot.hist(alpha=0.5)\n",
    "#pop_list.plot.hist('Population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEECAYAAADandTrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFp1JREFUeJzt3X9sU+e9x/HPsZ0fpsQpYJKSbIRBSDPWgsgoY+oum1ZR\nrtrSsg6itbvq2km9V9rWu06XrZ2qQqfdW7a7qZOqVeo2aetgnTIrbSmXSUytGEXtXZuqKVAI3NCl\nTfgNBpIskMRx/Nw/ElxoEvs44Tj48fslRbFPzjnfx1/hrx++fo7tGGOMAAA5zTfZAwAATBzFHAAs\nQDEHAAtQzAHAAhRzALAAxRwALBDwOkBnZ6caGhrU3t6ujRs3ptz31KlTikQi+uQnP6nDhw/rjjvu\n0Jw5c7weIgDkPM9n5gcPHtSSJUvkZjn7c889p6VLl+quu+7SnXfeqWeeecbr4QGAFTyfmS9btkz7\n9++/bNvhw4f18ssva/bs2Tp69KjuvvtulZeX68SJEwqHw5KksrIytbe3q7u7W6FQyOthAkBOm5Se\n+bPPPqsVK1bozjvv1PLly7Vp0yZJUm1trVpbWyVJ77//viSpt7d3MoYIADnF85n5aDo6OrRnzx4d\nOHBAsVhMxcXFkqT77rtP27Zt07Zt2zR16lSVlJRoxowZkzFEAMgpk1LMq6qq9LnPfU5VVVUaGBhQ\nU1OTJOns2bNatWqVioqKdOzYMS1cuFCBwKQMEQByiuP1B221tLTotdde0+7du3Xrrbdq1apVOnXq\nlLZt26brrrtO586d0+c//3nV1tZq586dam5u1rx589TT06O77rpLU6dO9XJ4AGAFz4s5AMB7XDQE\nABagmAOABSjmAGABz5eKHDt2zOsQOSscDisajU72MK5a5Cc18pNaLuenoqIi42OYmQOABSjmAGAB\nijkAWIBiDgAWoJgDgAUo5gBgAYo5AFiAYg4AFqCYA4AFKOYAYAGKOQBYgGIOABagmAOABTwv5nyR\nEQB4z/uZOcUcADxHMQcAC6T9copTp07pxz/+sWbMmCFJ6u3t1ezZs/Xtb3/bXQSTkOSfyBgBAGmk\nLebBYFAPPvigFi5cKEmKRCLJ264kEuMeHADAnbRtlpKSkmTxHhgYUFtbm2pra91HoM0CAJ5zTAbL\nTXbu3Cmfz6fly5dftj0SiaixsXHE/pFIRH1d5+QLXjPxkVooEAgoHo9P9jCuWuQnNfKTWi7np7Cw\nMONjMirmGzdu1Pe//30FAu6/B/ro+61ypkzNeGD5IJe/cDYbyE9q5Ce1XM6Pp1/ovH//ftXU1GRU\nyCXRZgGALHBdzF955RWtWLEi8wgJijkAeM31NPvhhx8eXwTDahYA8Jr3Fw2xNBEAPMcVoABggSwU\nc2bmAOA12iwAYAHaLABgAdosAGCBLLRZmJkDgNeYmQOABZiZA4AFmJkDgAVYmggAFmBpIgBYgDYL\nAFiAmTkAWICeOQBYgDYLAFiAdeYAYAFm5gBgAXrmAGABV98BeuzYMb3++usqLCzUgQMHtHbtWlVX\nV7uLwGoWAPBc2mKeSCT0+9//Xo888oh8Pp+++MUvyu/3u49AMQcAz6Ut5u+//74kafv27erv71dJ\nSYluueUW9xFoswCA59IW82g0qtbWVn33u9/VlClT9PTTTysQCOhLX/pScp9IJKLGxsYRx0YiEYVK\npqooHL6ig7ZFIBBQmNyMifykRn5Sy7f8pC3mwWBQFRUVmjJliiSptrZW+/fvv6yY19fXq76+ftTj\nu7u65ESjV2a0lgmHw4qSmzGRn9TIT2q5nJ+KioqMj0m7mmX+/Pnq6elRYrhdEo1GNWvWLPcRaLMA\ngOfSzsynTp2qr3/963ruuecUCoXU3d2tNWvWuI/AOnMA8JyrpYlLly7V0qVLxxeB1SwA4DnPLxoy\nXM4PAJ7LwhWgg56HAIB8x+eZA4AFKOYAYAE+aAsALMBH4AKABWizAIAFmJkDgAXomQOABWizAIAF\n+EJnALBAFmbmXAEKAF5jZg4AFqBnDgAWYGkiAFiApYkAYAHaLABgAdosAGABV18b99hjj6mgoECS\n5PP5tH79evcRaLMAgOdcFfNFixapvr5+fBFoswCA51wV846ODm3ZskWxWEzV1dWqq6tzH4F15gDg\nOVfFfPXq1aqurlYikdCGDRtUXFysBQsWJP8eiUTU2Ng44rhIJKJgsFgl4fCVG7FFAoGAwuRmTOQn\nNfKTWr7lxzEmsz7I888/r8LCQq1du9bV/keeflK+NfePZ2zWC4fDikajkz2Mqxb5SY38pJbL+amo\nqMj4mLSrWY4ePaodO3Yk7x8/flzl5eXuI9AzBwDPpW2zBINBNTc36+zZs+rt7VU4HNYXvvAF9xFY\nmggAnktbzKdPn65169aNPwJLEwHAc1wBCgAW4ApQALAAM3MAsACfmggAFmBmDgAWYGYOABagmAOA\nBWizAIAFWJoIABZgZg4AFqBnDgAW8LyYG9osAOA52iwAYAHaLABgAWbmAGABZuYAYAFm5gBgAWbm\nAGABrgAFAAu4LuaxWEzr1q3Tpk2bMotAmwUAPOe6mDc0NGjOnDmZR6DNAgCec1XMd+3apeuvv15l\nZWWZR6DNAgCeC6Tb4ciRIzpy5Ijuvfdetbe3j7pPJBJRY2PjqNsL/AFND4cnPlILBQIBhcnNmMhP\nauQntXzLj2NM6qb2iy++qEQioUAgoPfee0/xeFxLly7V7bff7irA4X//F/kf/e8rMljbhMNhRaPR\nyR7GVYv8pEZ+Usvl/FRUVGR8TNqZ+d133528HYvF1NfX57qQS6JnDgBZ4PoN0DfffFMHDhzQoUOH\n9Prrr7uPQDEHAM+lnZlftGzZMi1btizzCCxNBADPcQUoAFiAK0ABwAJ80BYAWIBiDgAWoGcOABag\nZw4AFqDNAgAWoM0CABagzQIAFsjCzJw2CwB4jZk5AFiAnjkAWIDVLABgAYo5AFiANgsAWIA3QAHA\nArRZAMACtFkAwAJpvzYukUjopz/9qebPn694PK4TJ07oW9/6lgoLC91FoM0CAJ5zNTOvqanRmjVr\n9LWvfU2xWExvvfWW+wi0WQDAc2mLuc/n01e/+lVJ0uDgoM6cOaOKigr3EbicHwA8l7bNctHu3bv1\n5z//WZ/97Gc1b9489xFMQsYYOY4znvEBAFxwjMmsD/LLX/5S8+fP18qVK5PbIpGIGhsbR+wbiUR0\n+PYlKnvhdTk+799rzTWBQEDxeHyyh3HVIj+pkZ/Ucjk/rt+TvETamfmRI0d06tQp1dXVSZLKysp0\n8uTJy/apr69XfX39mOeInj4tx+/PeHC2C4fDikajkz2Mqxb5SY38pJbL+cmolT0sbTEPBALasWOH\nPvjgAw0ODurIkSP65je/mVmUREKimAOAZ9IW8+uuu07r1q2bWBSWJwKAp7LTyGZ5IgB4KkvFnJk5\nAHgpO8WcS/oBwFO0WQDAAlmamVPMAcBLWZqZD2YlDADkK2bmAGABeuYAYAGWJgKABViaCAAWoM0C\nABagzQIAFqDNAgAWoM0CABZgnTkAWICeOQBYgJ45AFiAnjkAWIA2CwBYIO13gJ44cUINDQ2aO3eu\nzpw5o5KSEq1ZsyazKLRZAMBTaYt5T0+Pbr75Zt10002SpO9973uqq6vT3Llz3UehzQIAnkpbzKur\nqy+7b4xRUVFRZlFoswCAp9IW80s1NTVp0aJFqqysvGx7JBJRY2PjiP0jkYgkqTQUUmE4PIFh2ikQ\nCChMXsZEflIjP6nlW34cY9z1QPbt26empibdf//98vncv296+PYl8q37LznX3zjuQdoqHA4rGo1O\n9jCuWuQnNfKTWi7np6KiIuNjXFXl5uZm7dmzRw888IA6OzvV2tqaWRTeAAUAT6Vts7S1tekXv/iF\n5s2bpx/96Efq7+/XypUrVVNT4z4Kb4ACgKfSFvO5c+dq8+bNE4vCzBwAPMUVoABgAa4ABQALMDMH\nAAvwqYkAYAHaLABgAdosAGCBrBRzQ5sFADxFzxwALECbBQAsQDEHAAvQZgEAC2SnmH/wf3L5SbsA\ngHHwvJg7X7pN5rXtMttf9DoUAOQt74v5Pf8q1dwg87+veh0KAPKW98Xc55NTXiH19nodCgDyVnZ6\n5sVBqe9CVkIBQD7KXjHv75NJDGYlHADkmywV8ylDv/v6shIOAPJNdop58GIxp9UCAF5IW8w7Ozv1\n7LPP6oc//OH4o1ycmfMmKAB4Im0xP3jwoJYsWTKhi36cYHDoBjNzAPBE2mK+bNkyBS8W4/EqHj6+\nl2IOAF4IXImTRCIRNTY2jro9HA5r4HylzkoqKQyoOBy+EiGtEAgEFCYfYyI/qZGf1PItP1ekmNfX\n16u+vn7Uv0WjUZm+fklS96mT6olGr0RIK4TDYUXJx5jIT2rkJ7Vczk9FRUXGx2R5aSJtFgDwQtpi\n3tLSol27duncuXN64YUXFIvFMo+S7JmzmgUAvJC2zbJgwQItWLBgQkEcv18qLJT6KOYA4IXstFmk\noVYLbRYA8ER2izlLEwHAE9kr5sEpMrRZAMATWZyZB5mZA4BHslvMmZkDgCeyVsydIG+AAoBXWM0C\nABbI4hugQS4aAgCPZHdmPhiXGRjIWkgAyBfZfQNUotUCAB7I7sxcYnkiAHggu6tZJGbmAOCBrF4B\nKknm+JGshQSAfJG9Yv6p66XKKpnnn5U5fjhrYQEgH2SvzVJUJN9Dj0uBgBJPPS5zrCNboQHAetmb\nmUtyZpTJ9x//KRmjxE8eUWLzM0rs+ovMoRYZY7I5FACwyhX5DtBMOJVV8v3gJzIvbZZ5a5e06y8y\nklRZJeefVsq56WY5oWnZHhYA5LSsF3NJcspmyfm3H8gMDkqdZ2UO7pXZ8T8yDb+Wafi19Ik5Uuk0\nOZ+YI2f5P0uha6XCIjm+rP5HAgByhqtivnfvXjU1NSkUCslxHK1du/aKBHf8fmnGTDk33yLdfIvM\n0XaZ3W/JHNov9fxD5pWXZf7y0tDOBYXStLBUUCDnuk9IlVWS3y85PsnnSP6AnMoqqWyWVFQsFQfl\nBAquyDgB4GqXtpj39/frN7/5jZ566ikVFBTo5z//ud577z3deOONV3wwTmXVUEEeZs6cltn9lhSP\nSV3nhmbxsX6ZDw9J77wx4vgRXXd/YOjK06LiZIFXYdHwT+FQsQ8USAXDv5M/gctv+/1D5/L75fgv\n3h/+8Q3/XHo/+ds39GLj90s+3/C+vuRt098vE49LPh//6wAwIWmLeWtrq2bOnKmCgqFZbm1trZqb\nmz0p5h/nzJgp55Y7Rv2bicclYyQZKWGkWJ/U0SbTeWboc9P7eqX+XqmvT+rrlekf+q1Yv9R1VorF\nZAbj0sCAFL/0J55yTFfybdpTl95xnKEiP2rxv+RFwHGG9/Vfcnt4uxzJ0SW3nY/OPdZtt8eMez/J\nubh/ct+R+yTPecnt7uJiJfr7XRyTOv5o53a/36WPcYxjPs4ZbdvITa6PHWPfCyVTlejpcXG82/Fk\nMKYJP57RDnd7vLvx9E4tGZmfMeOMOiCX4xnDRB7PXfXu4wxLW8y7urpUXFycvB8MBtXV1XXZPpFI\nRI2NjSOOjUQiCofDGQ9q3D41b8KnMMZI8QGZ+IA0MPw7Hh/q78fjQy8Ag3FpcHBo2+Dg8Lbh24m4\nFB/elhiUEgkpkRjaN3Fxn4Q0OCifpMH4wPD2hEzio32USCT3U2I4ljGSSXz0N2M+imGMZMzQi83w\nbckMvfokb1/c/tE+5mP3LzvGJC47xqQ8ry6LMfZ53Z+zX5Jj3Jxz5DgyGWuu+sdkD+Aq1z3ZA5gI\nL4p5aWmp+vr6kvd7e3tVWlp62T719fWqrx89eDQazXhQVxdH8hUM/VzhFnw4HLYgPxOTap7z8fxk\nMCfK2NjFXxr1RSD5txEnGm2jq01j/r9vtHMaacaM6Tpz5myaOC7HM9bmiTyeMV8oJ5CPUXMxepxp\n06bp3LlzLva92h7PGGHSSFvMa2pqdPr0aQ0MDKigoEAHDx7UypUrxxcNuEo5l7ZrcoQvdK2cWOq2\nYD4LhMNyAkWTPYysSVvMi4qK9OCDD+p3v/udQqGQqqqqstIvBwC452pp4sKFC7Vw4UKvxwIAGCfW\nwwGABSjmAGABijkAWIBiDgAWoJgDgAUcwweJA0DO83RmPtZVoRhCflIjP6mRn9TyLT+0WQDAAhRz\nALAAxRwALEAxBwAL+J944oknvDq5MUaf+cxnvDp9ziM/qZGf1MhPavmWH5YmAoAFaLMAgAUo5gBg\nAVefZz4ee/fuVVNTk0KhkBzH0dq1a70KlRMee+yx5Jdi+3w+rV+/Xj09PXr++edVXl6u48eP6557\n7tG11147ySPNns7OTjU0NKi9vV0bN26UJMViMW3evFnTp0/X8ePHtXr1alVUVEiSdu3apQ8//FA+\nn0/l5eVasWLFZA7fU6PlZufOnXrllVeS/46+/OUva/ny5ZLyKzeSdOLECTU0NGju3Lk6c+aMSkpK\ntGbNmpTPqa1bt+rChQs6f/68Fi1apCVLlkzyo7jCjAf6+vrMd77zHROLxYwxxvzsZz8ze/fu9SJU\nzvjTn/40YtuvfvUr88YbbxhjjHn77bfN008/ne1hTaq//e1v5u233zaPPPJIcttLL71ktmzZYowx\npr293Tz++OPGGGOi0ahZt26dSSQSxhhjHn30UXPs2LHsDzpLRsvNX//6V3Py5MkR++Zbbowx5tCh\nQ6apqSl5/+GHHzZ///vfx3xOtba2mieffNIYY0w8HjcPPfSQOX/+fPYH7iFP2iytra2aOXNmcgZR\nW1ur5uZmL0LljI6ODm3ZskWRSCSZi3fffVc1NTWS8jNHy5YtUzAYvGxbc3NzMiezZ89We3u7Lly4\noD179mju3LlD39Upaf78+Xr33XezPuZsGS03krR9+3Zt3bpVjY2N6unpkaS8y40kVVdX66abbkre\nN8aoqKhozOfUO++8o/nz50uS/H6/Kisr1dLSkv2Be8iTNktXV5eKi4uT94PBoLq6urwIlTNWr16t\n6upqJRIJbdiwQcXFxerq6ko+YYPBoM6fP6/BwUH5/f5JHu3kuTQn0lBeuru7R2yfMmWKuru7J2OI\nk2bBggWqq6tTKBRSc3OznnrqKa1fvz7vc9PU1KRFixapsrJyzOdUd3e3Kisrk8fYWJM8mZmXlpaq\nr68veb+3t1elpaVehMoZ1dXVkob65bW1tdq/f79KS0vV29sraShH11xzTV4XckmX5UQayksoFBqx\n/cKFCwqFQpMxxElTVlaWfMw33HCDWlpalEgk8jo3+/bt0759+/SNb3xDksZ8ToVCoRH/rmyrSZ4U\n85qaGp0+fVoDAwOSpIMHD6qurs6LUDnh6NGj2rFjR/L+8ePHVV5ersWLF6u1tVUSObqorq4umZOO\njg5VVVVpypQpWrRokdra2mSGL4s4dOiQFi9ePJlDzbo//vGPGhwclDT0BuDMmTPl8/nyNjfNzc3a\ns2ePHnjgAXV2dqq1tXXM51RdXZ0OHTokSYrH4zp69Kg+/elPT9rYveDZRUN79+7Vm2++qVAoJL/f\nn9erWc6ePavf/va3mjNnjnp7ezU4OKj77rtPFy5c0B/+8AfNnDlTJ0+e1L333ptXq1laWlr02muv\naffu3br11lu1atUqSdKmTZs0bdo0nThxQl/5ylcuW83S1tYmn8+nWbNmWb1iY7TcvPrqq+ro6FBZ\nWZk6Ojp02223JfvD+ZQbSWpra9OGDRs0b948SVJ/f79WrlypJUuWjPmc2rp1q3p6enT+/HktXrzY\nutUsXAEKABbgoiEAsADFHAAsQDEHAAtQzAHAAhRzALAAxRwALEAxBwALUMwBwAL/D9YBxtwXjkJV\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b62908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}