stop_data_selected.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:08.482631Z",
     "start_time": "2020-02-08T09:20:08.480245Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:15.400203Z",
     "start_time": "2020-02-08T09:20:08.488024Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2020-02-07T12:51:25+01:00',\n",
       " '8_09868',\n",
       " '8__656___',\n",
       " '5306341',\n",
       " '2020-02-07T13:00:11+01:00']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for line in open('out', 'r'):\n",
    "    #print(line)\n",
    "    if (not 'ERROR' in line and line != '\\n'):\n",
    "        try:\n",
    "            line_json = json.loads(line)['stopTimes']\n",
    "            #print(line_json)\n",
    "            \n",
    "            for time in line_json['times']['Time']:\n",
    "                filtered = [line_json['actualDate'],\n",
    "                            line_json['stop']['codStop'],\n",
    "                            time['line']['codLine'],\n",
    "                            time['codIssue'],\n",
    "                            time['time']\n",
    "                           ]\n",
    "                #print(filtered)\n",
    "                data.append(filtered)\n",
    "        except Exception as e:\n",
    "            pass\n",
    "            #print(e)\n",
    "            #print(\"ERROR in line: \" + line)\n",
    "\n",
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:15.999452Z",
     "start_time": "2020-02-08T09:20:15.401386Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data, columns=(\"actualTime\", \"codStop\", \"codLine\", \"codIssue\", \"time\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:16.006943Z",
     "start_time": "2020-02-08T09:20:16.001009Z"
    }
   },
   "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>actualTime</th>\n",
       "      <th>codStop</th>\n",
       "      <th>codLine</th>\n",
       "      <th>codIssue</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-07T12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__656___</td>\n",
       "      <td>5306341</td>\n",
       "      <td>2020-02-07T13:00:11+01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-07T12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__656___</td>\n",
       "      <td>5305592</td>\n",
       "      <td>2020-02-07T13:06:00+01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-07T12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__656___</td>\n",
       "      <td>5305746</td>\n",
       "      <td>2020-02-07T13:16:00+01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-07T12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__815___</td>\n",
       "      <td>5306331</td>\n",
       "      <td>2020-02-07T13:28:38+01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-07T12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__815___</td>\n",
       "      <td>5306611</td>\n",
       "      <td>2020-02-07T14:27:00+01:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  actualTime  codStop    codLine codIssue  \\\n",
       "0  2020-02-07T12:51:25+01:00  8_09868  8__656___  5306341   \n",
       "1  2020-02-07T12:51:25+01:00  8_09868  8__656___  5305592   \n",
       "2  2020-02-07T12:51:25+01:00  8_09868  8__656___  5305746   \n",
       "3  2020-02-07T12:51:25+01:00  8_09868  8__815___  5306331   \n",
       "4  2020-02-07T12:51:25+01:00  8_09868  8__815___  5306611   \n",
       "\n",
       "                        time  \n",
       "0  2020-02-07T13:00:11+01:00  \n",
       "1  2020-02-07T13:06:00+01:00  \n",
       "2  2020-02-07T13:16:00+01:00  \n",
       "3  2020-02-07T13:28:38+01:00  \n",
       "4  2020-02-07T14:27:00+01:00  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:25.613851Z",
     "start_time": "2020-02-08T09:20:16.008308Z"
    }
   },
   "outputs": [],
   "source": [
    "df['time'] =pd.to_datetime(df['time'])\n",
    "df['actualTime'] =pd.to_datetime(df['actualTime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:25.637228Z",
     "start_time": "2020-02-08T09:20:25.614845Z"
    }
   },
   "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>actualTime</th>\n",
       "      <th>codStop</th>\n",
       "      <th>codLine</th>\n",
       "      <th>codIssue</th>\n",
       "      <th>time</th>\n",
       "      <th>remainingSeconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-07 12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__656___</td>\n",
       "      <td>5306341</td>\n",
       "      <td>2020-02-07 13:00:11+01:00</td>\n",
       "      <td>526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-07 12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__656___</td>\n",
       "      <td>5305592</td>\n",
       "      <td>2020-02-07 13:06:00+01:00</td>\n",
       "      <td>875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-07 12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__656___</td>\n",
       "      <td>5305746</td>\n",
       "      <td>2020-02-07 13:16:00+01:00</td>\n",
       "      <td>1475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-07 12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__815___</td>\n",
       "      <td>5306331</td>\n",
       "      <td>2020-02-07 13:28:38+01:00</td>\n",
       "      <td>2233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-07 12:51:25+01:00</td>\n",
       "      <td>8_09868</td>\n",
       "      <td>8__815___</td>\n",
       "      <td>5306611</td>\n",
       "      <td>2020-02-07 14:27:00+01:00</td>\n",
       "      <td>5735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 actualTime  codStop    codLine codIssue  \\\n",
       "0 2020-02-07 12:51:25+01:00  8_09868  8__656___  5306341   \n",
       "1 2020-02-07 12:51:25+01:00  8_09868  8__656___  5305592   \n",
       "2 2020-02-07 12:51:25+01:00  8_09868  8__656___  5305746   \n",
       "3 2020-02-07 12:51:25+01:00  8_09868  8__815___  5306331   \n",
       "4 2020-02-07 12:51:25+01:00  8_09868  8__815___  5306611   \n",
       "\n",
       "                       time  remainingSeconds  \n",
       "0 2020-02-07 13:00:11+01:00             526.0  \n",
       "1 2020-02-07 13:06:00+01:00             875.0  \n",
       "2 2020-02-07 13:16:00+01:00            1475.0  \n",
       "3 2020-02-07 13:28:38+01:00            2233.0  \n",
       "4 2020-02-07 14:27:00+01:00            5735.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['remainingSeconds'] = (df['time'] - df['actualTime']).astype('timedelta64[s]')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:25.658564Z",
     "start_time": "2020-02-08T09:20:25.638408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efb92cb09d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['remainingSeconds'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:25.687227Z",
     "start_time": "2020-02-08T09:20:25.659454Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    909826.000000\n",
       "mean       8563.873515\n",
       "std       12849.289745\n",
       "min           1.000000\n",
       "25%        1159.000000\n",
       "50%        3021.000000\n",
       "75%        8847.750000\n",
       "max       86406.000000\n",
       "Name: remainingSeconds, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['remainingSeconds'].describe()\n",
    "#df['remainingSecs'] = (datetime.datetime.fromisoformat(df['time']) - datetime.datetime.fromisoformat(df['actualTime'])).total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:25.703455Z",
     "start_time": "2020-02-08T09:20:25.688238Z"
    }
   },
   "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>actualTime</th>\n",
       "      <th>codStop</th>\n",
       "      <th>codLine</th>\n",
       "      <th>codIssue</th>\n",
       "      <th>time</th>\n",
       "      <th>remainingSeconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9308</th>\n",
       "      <td>2020-02-07 12:59:53+01:00</td>\n",
       "      <td>8_06002</td>\n",
       "      <td>8__661_A__</td>\n",
       "      <td>8__661_A___1_13:00:00_1_-__4_8__661_A__</td>\n",
       "      <td>2020-02-07 13:00:00+01:00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9309</th>\n",
       "      <td>2020-02-07 12:59:53+01:00</td>\n",
       "      <td>8_06002</td>\n",
       "      <td>8__661___</td>\n",
       "      <td>8__661____1_13:00:00_1_-__21_8__661___</td>\n",
       "      <td>2020-02-07 13:00:00+01:00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23458</th>\n",
       "      <td>2020-02-07 13:09:57+01:00</td>\n",
       "      <td>8_06002</td>\n",
       "      <td>8__661___</td>\n",
       "      <td>8__661____1_13:10:00_1_-__22_8__661___</td>\n",
       "      <td>2020-02-07 13:10:00+01:00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71622</th>\n",
       "      <td>2020-02-07 13:48:40+01:00</td>\n",
       "      <td>8_1333</td>\n",
       "      <td>8__627___</td>\n",
       "      <td>8__627____1_12:36:00_2_u.__36_8__627___</td>\n",
       "      <td>2020-02-07 13:48:49+01:00</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108351</th>\n",
       "      <td>2020-02-07 14:17:42+01:00</td>\n",
       "      <td>8_1333</td>\n",
       "      <td>8__627___</td>\n",
       "      <td>8__627____1_13:00:00_2_u.__38_8__627___</td>\n",
       "      <td>2020-02-07 14:17:52+01:00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      actualTime  codStop     codLine  \\\n",
       "9308   2020-02-07 12:59:53+01:00  8_06002  8__661_A__   \n",
       "9309   2020-02-07 12:59:53+01:00  8_06002   8__661___   \n",
       "23458  2020-02-07 13:09:57+01:00  8_06002   8__661___   \n",
       "71622  2020-02-07 13:48:40+01:00   8_1333   8__627___   \n",
       "108351 2020-02-07 14:17:42+01:00   8_1333   8__627___   \n",
       "\n",
       "                                       codIssue                      time  \\\n",
       "9308    8__661_A___1_13:00:00_1_-__4_8__661_A__ 2020-02-07 13:00:00+01:00   \n",
       "9309     8__661____1_13:00:00_1_-__21_8__661___ 2020-02-07 13:00:00+01:00   \n",
       "23458    8__661____1_13:10:00_1_-__22_8__661___ 2020-02-07 13:10:00+01:00   \n",
       "71622   8__627____1_12:36:00_2_u.__36_8__627___ 2020-02-07 13:48:49+01:00   \n",
       "108351  8__627____1_13:00:00_2_u.__38_8__627___ 2020-02-07 14:17:52+01:00   \n",
       "\n",
       "        remainingSeconds  \n",
       "9308                 7.0  \n",
       "9309                 7.0  \n",
       "23458                3.0  \n",
       "71622                9.0  \n",
       "108351              10.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('remainingSeconds <= 10').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:25.722986Z",
     "start_time": "2020-02-08T09:20:25.705407Z"
    }
   },
   "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>remainingSeconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21947.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59.928601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.676485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       remainingSeconds\n",
       "count      21947.000000\n",
       "mean          59.928601\n",
       "std           20.676485\n",
       "min            1.000000\n",
       "25%           40.000000\n",
       "50%           58.000000\n",
       "75%           78.000000\n",
       "max           99.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('remainingSeconds < 100').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:25.788153Z",
     "start_time": "2020-02-08T09:20:25.724041Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    0.0\n",
       "mean     NaN\n",
       "std      NaN\n",
       "min      NaN\n",
       "25%      NaN\n",
       "50%      NaN\n",
       "75%      NaN\n",
       "max      NaN\n",
       "Name: remainingSeconds, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"codIssue == '5266999'\")['remainingSeconds'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:25.940093Z",
     "start_time": "2020-02-08T09:20:25.789751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5307611      15273\n",
       "5307610      15117\n",
       "5307614      13674\n",
       "5307613      13618\n",
       "5307609      13137\n",
       "             ...  \n",
       "452432           1\n",
       "892418614        1\n",
       "808466992        1\n",
       "5354561          1\n",
       "5354574          1\n",
       "Name: codIssue, Length: 3869, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['codIssue'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:26.514562Z",
     "start_time": "2020-02-08T09:20:25.941801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3503     2020-02-07 12:54:12+01:00\n",
       "2758     2020-02-07 12:54:15+01:00\n",
       "14197    2020-02-07 13:03:47+01:00\n",
       "32374    2020-02-07 13:15:59+01:00\n",
       "71043    2020-02-07 13:47:59+01:00\n",
       "76253    2020-02-07 13:52:54+01:00\n",
       "88383    2020-02-07 14:01:19+01:00\n",
       "130210   2020-02-07 14:36:35+01:00\n",
       "132535   2020-02-07 14:39:56+01:00\n",
       "165499   2020-02-07 15:06:37+01:00\n",
       "165232   2020-02-07 15:06:37+01:00\n",
       "187761   2020-02-07 15:25:15+01:00\n",
       "204809   2020-02-07 15:38:58+01:00\n",
       "217744   2020-02-07 15:47:16+01:00\n",
       "221880   2020-02-07 15:50:35+01:00\n",
       "225141   2020-02-07 15:54:01+01:00\n",
       "289177   2020-02-07 16:42:40+01:00\n",
       "302415   2020-02-07 16:53:07+01:00\n",
       "315365   2020-02-07 17:03:09+01:00\n",
       "321035   2020-02-07 17:07:53+01:00\n",
       "341571   2020-02-07 17:23:33+01:00\n",
       "375136   2020-02-07 17:50:06+01:00\n",
       "395917   2020-02-07 18:07:19+01:00\n",
       "430108   2020-02-07 18:32:10+01:00\n",
       "428973   2020-02-07 18:32:12+01:00\n",
       "433193   2020-02-07 18:35:26+01:00\n",
       "437090   2020-02-07 18:37:22+01:00\n",
       "495523   2020-02-07 19:22:47+01:00\n",
       "507362   2020-02-07 19:32:45+01:00\n",
       "542056   2020-02-07 20:02:59+01:00\n",
       "547650   2020-02-07 20:06:49+01:00\n",
       "567097   2020-02-07 20:22:01+01:00\n",
       "575649   2020-02-07 20:28:13+01:00\n",
       "606333   2020-02-07 20:52:09+01:00\n",
       "611965   2020-02-07 20:56:54+01:00\n",
       "617691   2020-02-07 21:02:15+01:00\n",
       "645333   2020-02-07 21:25:27+01:00\n",
       "678048   2020-02-07 21:54:08+01:00\n",
       "689848   2020-02-07 22:04:21+01:00\n",
       "707473   2020-02-07 22:21:00+01:00\n",
       "709538   2020-02-07 22:22:43+01:00\n",
       "716361   2020-02-07 22:29:39+01:00\n",
       "723882   2020-02-07 22:36:22+01:00\n",
       "746406   2020-02-07 23:01:45+01:00\n",
       "770664   2020-02-07 23:36:51+01:00\n",
       "849653   2020-02-08 07:34:00+01:00\n",
       "849654   2020-02-08 08:34:00+01:00\n",
       "849658   2020-02-08 08:59:00+01:00\n",
       "852436   2020-02-08 09:30:02+01:00\n",
       "858365   2020-02-08 09:34:39+01:00\n",
       "889605   2020-02-08 10:00:00+01:00\n",
       "897378   2020-02-08 10:12:34+01:00\n",
       "906116   2020-02-08 10:17:39+01:00\n",
       "909060   2020-02-08 11:00:00+01:00\n",
       "907952   2020-02-08 12:00:00+01:00\n",
       "909062   2020-02-08 12:00:00+01:00\n",
       "909063   2020-02-08 12:45:00+01:00\n",
       "907969   2020-02-08 13:00:00+01:00\n",
       "Name: time, dtype: datetime64[ns, pytz.FixedOffset(60)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.groupby('codIssue')['remainingSeconds'].idxmin()].query(\"codLine == '8__658___'\").sort_values(by='time')['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:27.174067Z",
     "start_time": "2020-02-08T09:20:26.516664Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8__656___     151\n",
       "8__651___     134\n",
       "8__656_A__    131\n",
       "8__631___     129\n",
       "8__657___     120\n",
       "             ... \n",
       "8_N_602___      4\n",
       "8_N_901___      3\n",
       "8_N_906___      3\n",
       "8__532___       1\n",
       "8__574___       1\n",
       "Name: codLine, Length: 75, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"remainingSeconds < 3600\").loc[df.groupby('codIssue')['remainingSeconds'].idxmin()]['codLine'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:27.180141Z",
     "start_time": "2020-02-08T09:20:27.175306Z"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:27.448153Z",
     "start_time": "2020-02-08T09:20:27.181745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"639.9999861283738\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([737462.45833333, 737462.58333333, 737462.70833333, 737462.83333333,\n",
       "        737462.95833333, 737463.08333333, 737463.20833333, 737463.33333333]),\n",
       " <a list of 8 Text xticklabel objects>)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted = df.sort_values('actualTime', ascending=True).query(\"codLine == '8__656___' & codStop == '8_06297' &remainingSeconds < 1200 & actualTime > '2019-12-30 10:00:00+00'  & actualTime < '2019-12-30  10:30:00+00'\")\n",
    "plt.plot(df['actualTime'], df['remainingSeconds']/60, '.')\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:20:27.451260Z",
     "start_time": "2020-02-08T09:20:27.449151Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn\n",
    "from  matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-08T09:21:25.542905Z",
     "start_time": "2020-02-08T09:21:25.464656Z"
    }
   },
   "outputs": [],
   "source": [
    "fg = seaborn.FacetGrid(data=df_sorted, hue='codIssue')\n",
    "fg.map(pyplot.scatter, 'actualTime', 'remainingSeconds').add_legend()"
   ]
  },
  {
   "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": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}