transporto-velavimai.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'transportas-velavimai'...\n",
      "remote: Counting objects: 556, done.\u001b[K\n",
      "remote: Compressing objects: 100% (30/30), done.\u001b[K\n",
      "remote: Total 556 (delta 15), reused 0 (delta 0), pack-reused 526\u001b[K\n",
      "Receiving objects: 100% (556/556), 13.89 MiB | 2.90 MiB/s, done.\n",
      "Resolving deltas: 100% (353/353), done.\n",
      "Checking connectivity... done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/vilnius/transportas-velavimai.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import pandas as pd\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "frames = []\n",
    "for path in pathlib.Path('transportas-velavimai/rpt3/').glob('2*.tsv'):\n",
    "    d = pd.read_csv(str(path), encoding='utf-8-sig', sep='\\t')\n",
    "    d['Date'] = path.name[:-4]\n",
    "    frames.append(d)\n",
    "traffic = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Operator                         object\n",
       "Transportas                      object\n",
       "Marsrutas                        object\n",
       "MarsrutasOrd                      int64\n",
       "KryptiesPavadinimas              object\n",
       "TvarkarascioMarsrutas            object\n",
       "Pavadinimas                      object\n",
       "VehicleID                        object\n",
       "VehicleGarNum                    object\n",
       "PlanTrips                         int64\n",
       "FaktTrips                         int64\n",
       "RegularTrips                      int64\n",
       "FaktRunID                         int64\n",
       "PlanRunID                         int64\n",
       "RegularTripsEnds                  int64\n",
       "Date                     datetime64[ns]\n",
       "fakt_proc                       float64\n",
       "not_late                        float64\n",
       "effectiveness                   float64\n",
       "weekday                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = traffic\n",
    "d = d[d.Marsrutas != '---------']\n",
    "d = d.apply(partial(pd.to_numeric, errors='ignore'))\n",
    "d['fakt_proc'] = d.FaktTrips / d.PlanTrips\n",
    "d['not_late'] = d.RegularTrips / d.FaktTrips\n",
    "d['effectiveness'] = d.RegularTrips / d.PlanTrips\n",
    "d = d.dropna(subset=('effectiveness', 'not_late'))\n",
    "d['Date'] = pd.to_datetime(d['Date'])\n",
    "d['weekday'] = d.Date.dt.weekday\n",
    "d.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f46c94e3ac8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m8suB++/31z5v40ZJVPTrZ/+cceOAurpoWiNTjJOiImwx/txz/sT4uecCTzwh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\njJOiQMf97AohuV03HsV6cY2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xGzEOJLduvKmpcAbDft7D3PXigHyvGxr8HYOTGB82TO4X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27PlvRSZjuf08CDJPYZ1+l95uPJ/7skj8nzPlO0tuH4rIOfZ/KKUsnf58HMdpW/b2o5Pnu1vJpk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\n6LD/D72syDDuPyGWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46c94e13c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.groupby([d.Date, d.Operator]).agg({'effectiveness': 'mean'}).plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Operator</th>\n",
       "      <td>--------</td>\n",
       "      <td>VVT</td>\n",
       "      <td>VVT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportas</th>\n",
       "      <td>-----------</td>\n",
       "      <td>Autobusai</td>\n",
       "      <td>Autobusai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marsrutas</th>\n",
       "      <td>---------</td>\n",
       "      <td>1G</td>\n",
       "      <td>1G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarsrutasOrd</th>\n",
       "      <td>------------</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KryptiesPavadinimas</th>\n",
       "      <td>-------------------</td>\n",
       "      <td>Stotis–Kalvarijų g.–Santariškės</td>\n",
       "      <td>Stotis–Kalvarijų g.–Santariškės</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TvarkarascioMarsrutas</th>\n",
       "      <td>---------------------</td>\n",
       "      <td>1G</td>\n",
       "      <td>1G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pavadinimas</th>\n",
       "      <td>-----------</td>\n",
       "      <td>01-ž</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VehicleID</th>\n",
       "      <td>---------</td>\n",
       "      <td>2-10636</td>\n",
       "      <td>2-1918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VehicleGarNum</th>\n",
       "      <td>-------------</td>\n",
       "      <td>807</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PlanTrips</th>\n",
       "      <td>---------</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FaktTrips</th>\n",
       "      <td>---------</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RegularTrips</th>\n",
       "      <td>------------</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FaktRunID</th>\n",
       "      <td>---------</td>\n",
       "      <td>562821</td>\n",
       "      <td>562989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PlanRunID</th>\n",
       "      <td>---------</td>\n",
       "      <td>539446</td>\n",
       "      <td>539445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RegularTripsEnds</th>\n",
       "      <td>----------------</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>2016-05-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0                                1  \\\n",
       "Operator                            --------                              VVT   \n",
       "Transportas                      -----------                        Autobusai   \n",
       "Marsrutas                          ---------                               1G   \n",
       "MarsrutasOrd                    ------------                                1   \n",
       "KryptiesPavadinimas      -------------------  Stotis–Kalvarijų g.–Santariškės   \n",
       "TvarkarascioMarsrutas  ---------------------                               1G   \n",
       "Pavadinimas                      -----------                             01-ž   \n",
       "VehicleID                          ---------                          2-10636   \n",
       "VehicleGarNum                  -------------                              807   \n",
       "PlanTrips                          ---------                               20   \n",
       "FaktTrips                          ---------                               20   \n",
       "RegularTrips                    ------------                               14   \n",
       "FaktRunID                          ---------                           562821   \n",
       "PlanRunID                          ---------                           539446   \n",
       "RegularTripsEnds            ----------------                               17   \n",
       "Date                              2016-05-08                       2016-05-08   \n",
       "\n",
       "                                                     2  \n",
       "Operator                                           VVT  \n",
       "Transportas                                  Autobusai  \n",
       "Marsrutas                                           1G  \n",
       "MarsrutasOrd                                         1  \n",
       "KryptiesPavadinimas    Stotis–Kalvarijų g.–Santariškės  \n",
       "TvarkarascioMarsrutas                               1G  \n",
       "Pavadinimas                                         02  \n",
       "VehicleID                                       2-1918  \n",
       "VehicleGarNum                                      784  \n",
       "PlanTrips                                           10  \n",
       "FaktTrips                                           10  \n",
       "RegularTrips                                         9  \n",
       "FaktRunID                                       562989  \n",
       "PlanRunID                                       539445  \n",
       "RegularTripsEnds                                    10  \n",
       "Date                                        2016-05-08  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic.head(3).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Operator                 object\n",
       "Transportas              object\n",
       "Marsrutas                object\n",
       "MarsrutasOrd             object\n",
       "KryptiesPavadinimas      object\n",
       "TvarkarascioMarsrutas    object\n",
       "Pavadinimas              object\n",
       "VehicleID                object\n",
       "VehicleGarNum            object\n",
       "PlanTrips                object\n",
       "FaktTrips                object\n",
       "RegularTrips             object\n",
       "FaktRunID                object\n",
       "PlanRunID                object\n",
       "RegularTripsEnds         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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