{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markdown cell Title\n",
    "\n",
    "With **some** text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test\n"
     ]
    }
   ],
   "source": [
    "print(\"Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.368803</td>\n",
       "      <td>0.123778</td>\n",
       "      <td>0.718252</td>\n",
       "      <td>0.348959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.992440</td>\n",
       "      <td>1.691476</td>\n",
       "      <td>-0.673771</td>\n",
       "      <td>-0.078324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.569177</td>\n",
       "      <td>-0.305240</td>\n",
       "      <td>0.889811</td>\n",
       "      <td>1.044061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.056390</td>\n",
       "      <td>-0.040611</td>\n",
       "      <td>0.586828</td>\n",
       "      <td>0.621726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.295281</td>\n",
       "      <td>-0.451638</td>\n",
       "      <td>0.984592</td>\n",
       "      <td>-1.046666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.910541</td>\n",
       "      <td>2.867787</td>\n",
       "      <td>0.740802</td>\n",
       "      <td>-0.669767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  1.368803  0.123778  0.718252  0.348959\n",
       "2013-01-02  0.992440  1.691476 -0.673771 -0.078324\n",
       "2013-01-03  0.569177 -0.305240  0.889811  1.044061\n",
       "2013-01-04  0.056390 -0.040611  0.586828  0.621726\n",
       "2013-01-05  0.295281 -0.451638  0.984592 -1.046666\n",
       "2013-01-06 -0.910541  2.867787  0.740802 -0.669767"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "dates = pd.date_range('20130101',periods=6)\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XrwfgzTffZNGiRezcuZPjjz+eSZMm0ahRo1r9jCm9B2VmWcAKoCtwh7u/amaTgKvN7OfA88A0dz/sV3xmNhGYCNCxY8dUhilU/3HVH76zrdrbRTlWWX1RXFxMo0aNaNmyZWmvuiuvvJIPP/ywdJ2mTZvWenJKx/kDOofKGjhwIEuWLGHJkiVcfvnlbN68mSVLltCiRQtOOeUUXnrpJcaMGUNWVhZt27blq1/9Kq+99hrNmzenf//+pU2+xx13HKeddhoAvXv3ZtGiRQA899xzrFu3rvR4H3/8cenwSl//+tdp3LgxjRs35phjjmHLli3k5OTU6udLaYJy9wNAnpm1BB4xs17Aj4APgSOAu4ArgV+Us+1d4XL69eunmpdIFSTrVSd1U8l9qDfeeINevXrRoUMHZs2aRfPmzfn2t79dmmjKk/i4jQYNGpRON2jQoPTRGwcPHuSVV16hSZMmFW6fqsd1pOV3UO6+A1gEjHD3Ig/sBf4XyE9HDCL1QUmvutWrV7NmzRp+/vOfH7ZOeQOMSmYaOHAgjz/+OK1atSIrK4tWrVqxY8cOli5dysCBAzn11FOZP38+Bw4coLi4mBdeeIH8/Kpfck877TRuu+220ulVq9LbZSBlNSgzawPsc/cdZnYkMBy4zsyy3b3IghElvwmsSVUMIiJpU0m38FTo3bs3W7du5YILLjhk3q5du2jdujVnnXUWS5cu5cQTT8TM+PWvf027du148803q7T/W2+9lYsvvpg+ffqwf/9+Bg8enNbftKWyiS8bmBveh2oAPOjuj5vZwjB5GbAKuCiFMYiI1FlZWVl8/PHHh8ybM2dO6Xsz4/rrr+f6668/ZJ2yI3kkPnMtcVnr1q1Le/4lmjFjxiHTa9akpp6Ryl58q4G+5cwfmqpjSt2X7AF5Y8eOZfny5TRq1Ij8/Hxmz55d6z2KRCS9NJKEZJRkQ/mMHTuW++67D4ALLriAu+++m0mTJkUWZ42fBlyd0Qg27q7+NhBJU1RZ7S6YGXUIkgGUoCSjJBvKZ+TIkaXr5OfnU1hYGFWIUo+4ux7QWIHg57A1p9HMJeMke0AeBEnr3nvvZcSIERFGKPVBkyZN2LZt2+e+CNdV7s62bdvK7aJeVapBScYpGcpnx44dnHXWWaxZs6b0cQ7f+973GDx4MKeeemrEUUpdl5OTQ2FhIcXFxVGHEltNmjT5XD/eVYKSjJX4gLxevXpx1VVXUVxczOzZs6MOTeqBRo0a0blz56jDqNPUxCcZJdkD8u6++26efvpp5s2bR4MGOq1F6gLVoCSjJBvKp2HDhnTq1ImTTz4ZgLPPPrvcURREJHMoQUlGSfaAvFSMAyYi0VJbiIiIxJISlIiIxJISlIiIxJISlEgF9uzZQ35+PieeeCI9e/Zk+vTpALz77rsMGDCArl27ct555/HZZ59FHKlI3aNOElIjNRpLrbpjxsVgrLlkY//deOONTJ48mfPPP5+LLrqIe+65J+1j/y0ef1RajyeSbqpBiVQg2dh/Cxcu5JxzzgFg3LhxLFiwIMowReokJSiRSpQd+++4446jZcuWNGwYNEDk5OSwefPmiKMUqXuUoEQqUTL2X2FhIcuWLavy00hF5PNRghKpopKx/5YuXcqOHTtKfxxcWFhI+/btI45OpO5RghKpQHlj/3Xv3p0hQ4bw5z//GYC5c+cyatSoKMMUqZPUi0+kAsnG/uvRowfnn38+P/3pT+nbty8XXnhh1KGK1DlKUCIVSDb2X5cuXVi2bFkEEYnUHylr4jOzJma2zMxeN7O1ZnZVOL+zmb1qZm+b2XwzOyJVMYiISOZK5T2ovcBQdz8RyANGmNlXgOuAm9y9K7AdUNuIiIgcJmUJygO7wslG4cuBocCfw/lzgW+mKgYREclcKb0HZWZZwAqgK3AHsAHY4e4lD+8pBMrtn2tmE4GJAB07dkxlmCKVyp32RLXW//CdbTXabmOTaq0uUqeltJu5ux9w9zwgB8gHTqjGtne5ez9379emTZuUxSgiIvGUlt9BufsOYBFwMtDSzEpqbjmAxogREZHDpLIXXxszaxm+PxIYDqwnSFTnhKuNAx5NVQwiIpK5UnkPKhuYG96HagA86O6Pm9k64AEz+xWwErgnhTGIiEiGSlmCcvfVQN9y5r9DcD9KREQkKY3FJyKSIps2bWLIkCH06NGDnj17cssttwBw3nnnkZeXR15eHrm5ueTl5R2yXUFBAQUFBRFEHC8a6khEJEUaNmzIrFmzOOmkk9i5cydf/vKXGT58OPPnzy9dZ8qUKbRoUc2nRtcTSlAiIimSnZ1NdnY2AM2aNaN79+5s3ryZHj16AODuPPjggyxcuDDKMGNLTXwiImmwceNGVq5cyYABA0rnvfjii7Rt25Zu3bpFGFl8KUGJiKTYrl27GD16NDfffDPNmzcvnT9v3jzGjBkTYWTxpiY+EZEU2rdvH6NHj2bs2LGcffbZpfP379/Pww8/zIoVKyKMLt5Ug4qQeuqI1G3uzoUXXkj37t25/PLLD1n23HPPccIJJ5CTkxNRdPG/BilBiYikyMsvv8y9997LwoULS7uVP/nkkwA88MADat6rhJr4RERSZNCgQbh7ucvmzJmT3mAykGpQIiISS0pQIiISS0pQMZNsaJQZM2bQvn37w9qxRURqS9yuP7oHFTPJhkYBmDx5MldccUXEEabP4vFHRR2CSK2pztOVa/xE5plfr9b6ZcXt+qMEFTPJhkYREUm1uF1/1MQXY2WHRrn99tvp06cPEyZMYPv27RFHJyJ1WRyuP0pQMVV2aJRJkyaxYcMGVq1aRXZ2NlOmTIk6RBGpo+Jy/VGCiqHyhkZp27YtWVlZNGjQgO9+97ssW7Ys4ihFpC6K0/VHCSpmkg2NUlRUVPr+kUceoVevXlGEJyJ1WNyuP+okETMlQ6P07t279Cmb11xzDfPmzWPVqlWYGbm5ucyePTviSKUi7S6YGXUIItUWt+uPElTMJBsaZeTIkRFEIyL1SdyuPylr4jOzDma2yMzWmdlaM7s0nD/DzDab2arwpSuviIgcJpU1qP3AFHf/m5k1A1aY2bPhspvc/YYUHltERDJcyhKUuxcBReH7nWa2HmifquOJiEjdkpZefGaWC/QFXg1nfd/MVpvZ783si+mIQUREMkvKO0mYWVPgIeAyd//YzH4L/BLw8N9ZwIRytpsITATo2LFjqsOsHTNaVG/9jburv92Mf1XvGCKScWrcC7SOXYNSWoMys0YEyemP7v4wgLtvcfcD7n4Q+B2QX9627n6Xu/dz935t2rRJZZgiIhJDqezFZ8A9wHp3vzFhfnbCamcBa1IVg4iIZK5UNvGdAnwLeMPMVoXzfgyMMbM8gia+jcD/pDAGERHJUKnsxfcSYOUs0pP2RESkUhqLT0REYkkJSkREYkkJSkREYkkJSkREYkkJSkREYkkJSkREYkkJSkTqpIKCAgoKCqIOQz4HJSgREYklJSgREYklJSgREYklJSgREYmlKiUoM/vP8LHtmNlPzexhMzsptaGJiEh9VtUa1M/Cx7YPAr5G8BiN36YurNqzadMmhgwZQo8ePejZsye33HILAB999BHDhw+nW7duDB8+nO3btx+yXTp6AC0efxSLxx+V0mOIyL/V9HpQV8X9GlTVBHUg/PfrwF3u/gRwRGpCql0NGzZk1qxZrFu3jldeeYU77riDdevWMXPmTIYNG8Zbb73FsGHDmDmzhk+wFJGMoetBZqlqgtpsZrOB84AnzaxxNbaNVHZ2NiedFLRGNmvWjO7du7N582YeffRRxo0bB8C4ceNYsGBBlGGKSBroepBZqppkzgWeBk539x1AK2BqyqJKkY0bN7Jy5UoGDBjAli1byM4OHu7brl07tmzZEnF0IpJOuh7EX5USlLt/AvwTGBTO2g+8laqgUmHXrl2MHj2am2++mebNmx+yzMwInlAvIvWBrgeZoaq9+KYDVwI/Cmc1Au5LVVC1bd++fYwePZqxY8dy9tlnA9C2bVuKiooAKCoq4phjjokyRBFJE10PMkdVm/jOAs4EdgO4+wdAs1QFVZvcnQsvvJDu3btz+eWXl84/88wzmTt3LgBz585l1KhRUYUoImmi60FmaVjF9T5zdzczBzCz+PZLLOPll1/m3nvvpXfv3uTl5QFwzTXXMG3aNM4991zuueceOnXqxIMPPhhxpCKSaroeZJaqJqgHw158Lc3su8AE4HepC6v2DBo0CHcvd9nzzz+f5mhEJEq6HmSWKiUod7/BzIYDHwPHAz9392cr2sbMOgB/ANoCTvD7qVvMrBUwH8gFNgLnunv9+FWciIhUWaUJysyygOfcfQhQYVIqYz8wxd3/Fg6TtMLMngXGA8+7+0wzmwZMI+iAISIiUqrSThLufgA4aGYtqrNjdy9y97+F73cC64H2wChgbrjaXOCb1YpYRETqhareg9oFvBHWgHaXzHT3S6qysZnlAn2BV4G27l4ULvqQoAmwvG0mAhMBOnbsWMUwk8ud9kS11v/wnW3V3m5jk2odQkSqa0Y1vidv3F39bQBm/Kt660vKVDVBPRy+qs3MmgIPAZe5+8eJP4BL7BlYlrvfBdwF0K9fv/LvaoqISJ1V1U4Sc83sCOBL4ay/u/u+yrYzs0YEyemP7l6S4LaYWba7F5lZNsEIFSIiIoeo6kgSBQRDG90B/Ab4h5kNrmQbI3gsx3p3vzFh0WPAuPD9OODRasYsIiL1QFWb+GYBp7n73wHM7EvAPODLFWxzCvAtgntXq8J5PwZmEvyu6kLgPYKBaEVERA5R1QTVqCQ5Abj7P8Lmu6Tc/SUg2YiLw6p4XBERqaeqmqCWm9nd/HuA2LHA8tSEJCIiUvUENQm4GCjpVv4iwb0oERGRlKhqgmoI3FLS2SEcXaJxyqISEZF6r6qP23geODJh+kjgudoPR0REJFDVBNXE3XeVTITvv5CakERERKqeoHab2UklE2bWD/g0NSGJiIhU/R7UZcCfzOyDcDobOC81IcVDuwtmRh2CiEi9VmENysz6m1k7d38NOIHgOU77gKeAd9MQn4iI1FOVNfHNBj4L359MMBLEHcB2woFcRUREUqGyJr4sd/8ofH8ewVNxHwIeShi+SEREpNZVVoPKMrOSJDYMWJiwrKr3r0RERKqtsiQzD/irmW0l6LX3IoCZdQX0VC8Ria3F44+KOgT5nCpMUO5+tZk9T9Br7xl3L3lwYAPgB6kOTkRE6q9Km+nc/ZVy5v0jNeGIiIgEqvpDXRERkbRSghIRkVhSghIRkVhSghIRkVhSghIRkVhSghIRkVhKWYIys9+b2T/NbE3CvBlmttnMVoWvkak6voiIZLZU1qDmACPKmX+Tu+eFrydTeHwREclgKUtQ7v4C8FGlK4qIiJQjintQ3zez1WET4BeTrWRmE81suZktLy4uTmd8IiISA+lOUL8FjgPygCJgVrIV3f0ud+/n7v3atGmTrvhERCQm0pqg3H2Lux9w94PA74D8dB5fREQyR1oTlJllJ0yeBaxJtq6IiNRvKXvooJnNAwqA1mZWCEwHCswsD3BgI/A/qTq+iIhktpQlKHcfU87se1J1PBERqVs0koSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMSSEpSIiMRSyhKUmf3ezP5pZmsS5rUys2fN7K3w3y+m6vgiIpLZUlmDmgOMKDNvGvC8u3cDng+nRUREDpOyBOXuLwAflZk9Cpgbvp8LfDNVxxcRkcyW7ntQbd29KHz/IdA22YpmNtHMlpvZ8uLi4vREJyIisRFZJwl3d8ArWH6Xu/dz935t2rRJY2QiIhIH6U5QW8wsGyD8959pPr6IiGSIdCeox4Bx4ftxwKNpPr6IiGSIVHYznwcsBY43s0IzuxCYCQw3s7eAr4XTIiIih2mYqh27+5gki4al6pgiIlJ3aCQJERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSWoMAkYAAAFWUlEQVQoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJJSUoERGJpYZRHNTMNgI7gQPAfnfvF0UcIiISX5EkqNAQd98a4fFFRCTG1MQnIiKxFFWCcuAZM1thZhMjikFERGIsqia+Qe6+2cyOAZ41szfd/YXEFcLENRGgY8eOUcQoIiIRiqQG5e6bw3//CTwC5Jezzl3u3s/d+7Vp0ybdIYqISMTSnqDM7Cgza1byHjgNWJPuOEREJN6iaOJrCzxiZiXHv9/dn4ogDhERibG0Jyh3fwc4Md3HFRGRzKJu5iIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEktKUCIiEkuRJCgzG2Fmfzezt81sWhQxiIhIvKU9QZlZFnAHcAbQAxhjZj3SHYeIiMRbFDWofOBtd3/H3T8DHgBGRRCHiIjEmLl7eg9odg4wwt2/E05/Cxjg7t8vs95EYGI4eTzw97QGWjOtga1RBxFjKp/KqYwqpvKpWKaUTyd3b1PZSg3TEUlNuPtdwF1Rx1EdZrbc3ftFHUdcqXwqpzKqmMqnYnWtfKJo4tsMdEiYzgnniYiIlIoiQb0GdDOzzmZ2BHA+8FgEcYiISIylvYnP3feb2feBp4Es4PfuvjbdcaRIRjVJRkDlUzmVUcVUPhWrU+WT9k4SIiIiVaGRJEREJJaUoEREJJaUoGrIzNqa2f1m9o6ZrTCzpWZ2lpkdbWaLzGyXmd0edZxRqaB8hofTb4T/Do061ihUUD75ZrYqfL1uZmdFHWsUkpVPwvKO4f+xK6KMMyoVnD+5ZvZpwjl0Z9Sxfh6x/R1UnJmZAQuAue5+QTivE3AmsAf4GdArfNU7lZTPS8B/uPsHZtaLoLNM+8iCjUAl5fM00C/sTJQNvG5mf3H3/dFFnF6VlE+JG4H/iyC8yFVSPiuBDe6eF2GItUYJqmaGAp+5e+m3E3d/D7gtnHzJzLpGElk8VFY+JdYCR5pZY3ffm84AI1bV8mkC1MdeTBWWj5l9E3gX2B1NeJFLWj5mlhtVUKmgJr6a6Qn8LeogYqyq5TMa+Fs9S05QSfmY2QAzWwu8AVxUn2pPoaTlY2ZNgSuBq9IaUbxU9v+rs5mtNLO/mtmp6QoqFVSDqgVmdgcwiOBbTf+o44mb8srHzHoC1wGnRRlbHJQtH3d/FehpZt2BuWb2f+6+J9ooo5NYPsBfgZvcfVfQ0iVlymcQ0NHdt5nZl4EFZtbT3T+ONMgaUg2qZtYCJ5VMuPvFwDCg0sEP64kKy8fMcoBHgP929w2RRBitKp0/7r4e2EX9u5dZUfkMAH5tZhuBy4Afhz/8r0+Slo+773X3beH8FcAG4EuRRFkLlKBqZiHQxMwmJcz7QlTBxFDS8jGzlsATwDR3fzmK4GKgovLpbGYNw/edgBOAjWmPMFpJy8fdT3X3XHfPBW4GrnH3+tZbtqLzp034zD3MrAvQDXgn/SHWDo0kUUNhD6ubCL7RFRPcsL3T3eeH3+6aA0cAO4DT3H1dVLFGIVn5EPyH+RHwVsLqp7n7P9MeZIQqKJ8jgGnAPuAg8At3XxBVnFGp6P9XwjozgF3ufkMkQUaogvNnP/AL/n3+THf3v0QV5+elBCUiIrGkJj4REYklJSgREYklJSgREYklJSgREYklJSgREYklJSgREYklJSgREYml/we4qiG1fXq0VQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "men_means, men_std = (20, 35, 30, 35, 27), (2, 3, 4, 1, 2)\n",
    "women_means, women_std = (25, 32, 34, 20, 25), (3, 5, 2, 3, 3)\n",
    "\n",
    "ind = np.arange(len(men_means))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind - width/2, men_means, width, yerr=men_std,\n",
    "                label='Men')\n",
    "rects2 = ax.bar(ind + width/2, women_means, width, yerr=women_std,\n",
    "                label='Women')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Scores')\n",
    "ax.set_title('Scores by group and gender')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "def autolabel(rects, xpos='center'):\n",
    "    \"\"\"\n",
    "    Attach a text label above each bar in *rects*, displaying its height.\n",
    "\n",
    "    *xpos* indicates which side to place the text w.r.t. the center of\n",
    "    the bar. It can be one of the following {'center', 'right', 'left'}.\n",
    "    \"\"\"\n",
    "\n",
    "    ha = {'center': 'center', 'right': 'left', 'left': 'right'}\n",
    "    offset = {'center': 0, 'right': 1, 'left': -1}\n",
    "\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(offset[xpos]*3, 3),  # use 3 points offset\n",
    "                    textcoords=\"offset points\",  # in both directions\n",
    "                    ha=ha[xpos], va='bottom')\n",
    "\n",
    "\n",
    "autolabel(rects1, \"left\")\n",
    "autolabel(rects2, \"right\")\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the end of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}