LosantGists/writing-guide.ipynb

## writing-guide.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/taronfoxworth/src/analytics/input\n",
      "/Users/taronfoxworth/src/analytics/output\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "print(os.environ['INPUT_DIR'])\n",
    "print(os.environ['OUTPUT_DIR'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>ID</th>\n",
       "      <th>internal-temp</th>\n",
       "      <th>rpm</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-04-24 00:19:44</th>\n",
       "      <td>5c6daf145db1570006baef21</td>\n",
       "      <td>65</td>\n",
       "      <td>2281</td>\n",
       "      <td>39.1038666863143,-84.51791754239483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-24 00:19:46</th>\n",
       "      <td>5c6daf145db1570006baef21</td>\n",
       "      <td>72</td>\n",
       "      <td>2174</td>\n",
       "      <td>39.10361852901866,-84.51181897235745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-24 00:19:48</th>\n",
       "      <td>5c6daf145db1570006baef21</td>\n",
       "      <td>83</td>\n",
       "      <td>2987</td>\n",
       "      <td>39.10410090446387,-84.51090204885554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-24 00:19:50</th>\n",
       "      <td>5c6daf145db1570006baef21</td>\n",
       "      <td>71</td>\n",
       "      <td>2765</td>\n",
       "      <td>39.104399934002586,-84.51023546267568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-24 00:19:52</th>\n",
       "      <td>5c6daf145db1570006baef21</td>\n",
       "      <td>77</td>\n",
       "      <td>2462</td>\n",
       "      <td>39.10424648543921,-84.51776261768684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           ID  internal-temp   rpm  \\\n",
       "Timestamp                                                            \n",
       "2019-04-24 00:19:44  5c6daf145db1570006baef21             65  2281   \n",
       "2019-04-24 00:19:46  5c6daf145db1570006baef21             72  2174   \n",
       "2019-04-24 00:19:48  5c6daf145db1570006baef21             83  2987   \n",
       "2019-04-24 00:19:50  5c6daf145db1570006baef21             71  2765   \n",
       "2019-04-24 00:19:52  5c6daf145db1570006baef21             77  2462   \n",
       "\n",
       "                                                  location  \n",
       "Timestamp                                                   \n",
       "2019-04-24 00:19:44    39.1038666863143,-84.51791754239483  \n",
       "2019-04-24 00:19:46   39.10361852901866,-84.51181897235745  \n",
       "2019-04-24 00:19:48   39.10410090446387,-84.51090204885554  \n",
       "2019-04-24 00:19:50  39.104399934002586,-84.51023546267568  \n",
       "2019-04-24 00:19:52   39.10424648543921,-84.51776261768684  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(os.path.join(os.environ['INPUT_DIR'], 'data.csv'))\n",
    "\n",
    "data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='ms')\n",
    "data.set_index('Timestamp', inplace=True)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "histogram = sns.distplot(data[['internal-temp']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Save Histogram\n",
    "fig = histogram.get_figure()\n",
    "\n",
    "output_path = os.path.join(os.environ['OUTPUT_DIR'], 'histogram.png')\n",
    "fig.savefig(output_path)"
   ]
  }
 ],
 "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
}