{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A report on new breakthrough data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing data on an external file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "data = [\n",
    "    14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4,\\\n",
    "    16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2,\\\n",
    "    9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2,\\\n",
    "    12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7,\\\n",
    "    8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9,\\\n",
    "    16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6,\\\n",
    "    19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1,\\\n",
    "    13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3,\\\n",
    "    15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7,\\\n",
    "    11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9,\\\n",
    "    20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8,\\\n",
    "    22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4,\\\n",
    "    15.7, 10.2, 8.9, 21.0\n",
    "]\n",
    "\n",
    "np.savetxt(\"data.txt\", data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tabulate in /opt/conda/lib/python3.6/site-packages (0.8.7)\r\n"
     ]
    }
   ],
   "source": [
    "! pip install tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "<tr><td>Mean  </td><td style=\"text-align: right;\">14.113  </td></tr>\n",
       "<tr><td>Min   </td><td style=\"text-align: right;\"> 2.8    </td></tr>\n",
       "<tr><td>Max   </td><td style=\"text-align: right;\">23.4    </td></tr>\n",
       "<tr><td>Median</td><td style=\"text-align: right;\">14.5    </td></tr>\n",
       "<tr><td>Std   </td><td style=\"text-align: right;\"> 4.33409</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import HTML, display\n",
    "import tabulate\n",
    "from typing import List, Any\n",
    "\n",
    "def display_table(table: List[List[Any]]):\n",
    "    return display(HTML(tabulate.tabulate(table, tablefmt='html')))\n",
    "    \n",
    "    \n",
    "display_table([\n",
    "    [\"Mean\", np.mean(data)],\n",
    "    [\"Min\", np.min(data)],\n",
    "    [\"Max\", np.max(data)],\n",
    "    [\"Median\", np.median(data)],\n",
    "    [\"Std\", np.std(data, ddof=1)]\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, subs = plt.subplots(2)\n",
    "subs[0].plot(data)\n",
    "subs[0].grid(True, linestyle=\":\")\n",
    "subs[1].hist(data)\n",
    "subs[1].grid(True, linestyle=\":\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "nbformat": 4,
 "nbformat_minor": 2
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