http_status_code_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>request_date</th>\n",
       "      <th></th>\n",
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       "      <th>2016-09-29 10:28:36.516</th>\n",
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       "      <td>200</td>\n",
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       "      <th>2016-09-29 10:28:58.039</th>\n",
       "      <td>200</td>\n",
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      "text/plain": [
       "                         response_code\n",
       "request_date                          \n",
       "2016-09-29 10:28:36.516            200\n",
       "2016-09-29 10:28:36.724            200\n",
       "2016-09-29 10:21:57.558            200\n",
       "2016-09-29 10:21:58.194            200\n",
       "2016-09-29 10:28:58.039            200"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "http_response_data = pd.read_csv('http_response_status_data.csv', parse_dates=True, index_col='request_date')\n",
    "http_response_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def determine_response_class(row):\n",
    "    \"\"\"\n",
    "    Check the status code of a given row, and return a response class.\n",
    "    \n",
    "    - 2xx: 'success'\n",
    "    - 3xx: 'warning'\n",
    "    - 4xx: 'client_error'\n",
    "    - 5xx: 'server_error'\n",
    "    - NaN or 0: 'unknown'\n",
    "    \"\"\"\n",
    "    \n",
    "    response_code = row['response_code']\n",
    "    \n",
    "    if response_code >= 200 and response_code < 300:\n",
    "        return 'success'\n",
    "    elif response_code >= 300 and response_code < 400:\n",
    "        return 'warning'\n",
    "    elif response_code >= 400 and response_code < 500:\n",
    "        return 'client_error'\n",
    "    elif response_code >= 500 and response_code < 600:\n",
    "        return 'server_error'\n",
    "    else:\n",
    "        return 'unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>response_code</th>\n",
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       "      <td>200</td>\n",
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       "      <th>2016-09-29 13:01:15.492</th>\n",
       "      <td>200</td>\n",
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       "      <td>304</td>\n",
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       "      <td>201</td>\n",
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       "      <td>success</td>\n",
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       "      <td>200</td>\n",
       "      <td>success</td>\n",
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       "      <th>2016-09-30 10:15:10.650</th>\n",
       "      <td>204</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-21 15:16:27.755</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-21 15:15:56.283</th>\n",
       "      <td>200</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-21 15:30:21.204</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-21 15:30:49.613</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:44:30.742</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:31:43.059</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:31:12.723</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-21 15:36:30.079</th>\n",
       "      <td>204</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-21 15:36:17.058</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 06:57:55.291</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 06:49:59.075</th>\n",
       "      <td>500</td>\n",
       "      <td>server_error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:28:16.298</th>\n",
       "      <td>200</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:57:30.086</th>\n",
       "      <td>200</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:57:20.285</th>\n",
       "      <td>200</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:55:51.794</th>\n",
       "      <td>200</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:55:51.868</th>\n",
       "      <td>200</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:55:40.162</th>\n",
       "      <td>302</td>\n",
       "      <td>warning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:55:40.567</th>\n",
       "      <td>200</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 09:27:34.192</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 08:01:22.402</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:57:18.988</th>\n",
       "      <td>200</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 08:01:39.593</th>\n",
       "      <td>204</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 07:55:43.276</th>\n",
       "      <td>302</td>\n",
       "      <td>warning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 09:27:57.816</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 09:28:14.496</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 09:30:18.986</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 10:01:07.873</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 10:02:05.668</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 10:03:22.560</th>\n",
       "      <td>204</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-22 10:03:27.895</th>\n",
       "      <td>201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9863 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         response_code response_class\n",
       "request_date                                         \n",
       "2016-09-29 10:28:36.516            200        success\n",
       "2016-09-29 10:28:36.724            200        success\n",
       "2016-09-29 10:21:57.558            200        success\n",
       "2016-09-29 10:21:58.194            200        success\n",
       "2016-09-29 10:28:58.039            200        success\n",
       "2016-09-29 06:49:49.525            302        warning\n",
       "2016-09-29 11:32:54.275            201        success\n",
       "2016-09-29 11:32:54.410            201        success\n",
       "2016-09-29 06:56:47.073            200        success\n",
       "2016-09-29 06:48:47.424            302        warning\n",
       "2016-09-29 06:48:44.795            302        warning\n",
       "2016-09-29 06:56:42.678            302        warning\n",
       "2016-09-13 09:25:01.540            201        success\n",
       "2016-09-30 09:10:40.113            200        success\n",
       "2016-09-29 13:01:19.266            200        success\n",
       "2016-09-30 09:11:00.793            200        success\n",
       "2016-09-29 13:01:16.282            200        success\n",
       "2016-09-29 13:01:15.360            200        success\n",
       "2016-09-29 13:01:15.492            200        success\n",
       "2016-09-29 13:01:19.408            304        warning\n",
       "2016-09-29 13:17:17.887            201        success\n",
       "2016-09-30 07:28:51.100            201        success\n",
       "2016-09-30 07:26:26.904            201        success\n",
       "2016-09-30 10:01:15.844            200        success\n",
       "2016-09-30 10:01:15.855            304        warning\n",
       "2016-09-30 10:15:16.608            200        success\n",
       "2016-09-30 10:15:16.612            200        success\n",
       "2016-09-30 10:22:29.482            200        success\n",
       "2016-09-30 10:22:29.627            200        success\n",
       "2016-09-30 10:15:10.650            204        success\n",
       "...                                ...            ...\n",
       "2017-09-21 15:16:27.755            201        success\n",
       "2017-09-21 15:15:56.283            200        success\n",
       "2017-09-21 15:30:21.204            201        success\n",
       "2017-09-21 15:30:49.613            201        success\n",
       "2017-09-22 07:44:30.742            201        success\n",
       "2017-09-22 07:31:43.059            201        success\n",
       "2017-09-22 07:31:12.723            201        success\n",
       "2017-09-21 15:36:30.079            204        success\n",
       "2017-09-21 15:36:17.058            201        success\n",
       "2017-09-22 06:57:55.291            201        success\n",
       "2017-09-22 06:49:59.075            500   server_error\n",
       "2017-09-22 07:28:16.298            200        success\n",
       "2017-09-22 07:57:30.086            200        success\n",
       "2017-09-22 07:57:20.285            200        success\n",
       "2017-09-22 07:55:51.794            200        success\n",
       "2017-09-22 07:55:51.868            200        success\n",
       "2017-09-22 07:55:40.162            302        warning\n",
       "2017-09-22 07:55:40.567            200        success\n",
       "2017-09-22 09:27:34.192            201        success\n",
       "2017-09-22 08:01:22.402            201        success\n",
       "2017-09-22 07:57:18.988            200        success\n",
       "2017-09-22 08:01:39.593            204        success\n",
       "2017-09-22 07:55:43.276            302        warning\n",
       "2017-09-22 09:27:57.816            201        success\n",
       "2017-09-22 09:28:14.496            201        success\n",
       "2017-09-22 09:30:18.986            201        success\n",
       "2017-09-22 10:01:07.873            201        success\n",
       "2017-09-22 10:02:05.668            201        success\n",
       "2017-09-22 10:03:22.560            204        success\n",
       "2017-09-22 10:03:27.895            201        success\n",
       "\n",
       "[9863 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add 'Response class' column to API Logs, where response class is determined by HTTP status code\n",
    "http_response_data['response_class'] = http_response_data.apply(determine_response_class, axis='columns')\n",
    "\n",
    "http_response_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wImsN9F+uqp+oqiOmy08k+fIyCwMAAAD2ba2B/p9m9uflvpjk5iTPS/KSJdUEAAAA3IO1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b9a2b59b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the plot wider and taller\n",
    "plt.figure(figsize=(17,7))\n",
    "\n",
    "# Use seaborn to produce a countplot of response status codes\n",
    "status_code_counts_plot = sns.countplot(\n",
    "    x='response_code',\n",
    "    data=http_response_data,\n",
    "    color='teal',\n",
    "    saturation=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brylie/anaconda3/envs/tmp-http-visualization/lib/python3.5/site-packages/seaborn/axisgrid.py:703: UserWarning: Using the countplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1b9a1e8400>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8CLyqqh7qdziSJEmSNDqzFlhVdS3wW1O038IUVwGsqvuBw6fZ1ruBdw/fTUmSJEla/Ib6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\nliRJkiT15P8Dm0zA/7/rGIUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b9a1c8c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(http_response_data, col='response_class')\n",
    "\n",
    "grid.map(sns.countplot, 'response_code')"
   ]
  },
  {
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}