openbus_7_lagbaomer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work-in-Progress!\n",
    "Looking into schedule changes for LAG-BAOMER (ל\"ג בעומר) 2018.  \n",
    "Changes are (supposedly?) attributed to the [הילולת רבי שמעון בר יוחאי](https://he.wikipedia.org/wiki/%D7%94%D7%99%D7%9C%D7%95%D7%9C%D7%AA_%D7%A8%D7%91%D7%99_%D7%A9%D7%9E%D7%A2%D7%95%D7%9F_%D7%91%D7%A8_%D7%99%D7%95%D7%97%D7%90%D7%99) which takes place at Mount Meron."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from ftplib import FTP\n",
    "import datetime\n",
    "import re\n",
    "import zipfile\n",
    "import os\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15, 10) # set default size of plots\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "sns.set_context(\"talk\")\n",
    "sns.set_palette('Set1', 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data\n",
    "I get all the files from our GTFS repository at הסדנא - it is not still openly accessible, but you can ask me for files or access if you need it.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tidy it up\n",
    "Using [partridge](https://github.com/remix/partridge/tree/master/partridge) for filtering on dates, and then some tidying up and transformations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gtfs_utils import *\n",
    "\n",
    "TARIFF_FILE_NAME = 'Tariff.zip'\n",
    "TARIFF_TXT_NAME = 'Tariff.txt'\n",
    "TARIFF_TO_REFORM_ZONE = 'StationToReformZone.txt'\n",
    "local_tariff_path = 'data/sample/tariff.zip' \n",
    "\n",
    "conn = ftp_connect()\n",
    "get_ftp_file(conn, file_name = TARIFF_FILE_NAME, local_zip_path = local_tariff_path )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tariff stuff for zone names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not a true csv, so we need to jiggle it a bit\n",
    "cols = ['ShareCode','ShareCodeDesc','ZoneCodes','Daily','Weekly','Monthly','FromDate','ToDate', 'EXTRA']\n",
    "reform_cols = ['StationId', 'ReformZoneCode','FromDate','ToDate', 'EXTRA']\n",
    "with zipfile.ZipFile(local_tariff_path) as zf:\n",
    "    tariff_df = (pd.read_csv(zf.open(TARIFF_TXT_NAME), header=None, skiprows=[0], names = cols)\n",
    "                .drop(columns = ['EXTRA']))\n",
    "    reform_df = (pd.read_csv(zf.open(TARIFF_TO_REFORM_ZONE), header=None, skiprows=[0], names = reform_cols)\n",
    "                 .drop(columns = ['EXTRA']))\n",
    "\n",
    "    \n",
    "# remove ShareCodes which contain multiple zones  e.g. גוש דן מורחב\n",
    "tariff_df = (tariff_df[~ tariff_df.ZoneCodes.str.contains(';')]\n",
    "             .rename(columns = {'ShareCodeDesc': 'zone_name',\n",
    "                               'ZoneCodes': 'zone_id'}))\n",
    "rs = reform_df[['StationId', 'ReformZoneCode']].drop_duplicates().applymap(str).set_index('StationId').iloc[:,0]\n",
    "\n",
    "ts = (tariff_df[['zone_id', 'zone_name']].drop_duplicates().set_index('zone_id').iloc[:,0])\n",
    "zones = rs.map(ts).reset_index().rename(columns={'StationId': 'stop_code', 'ReformZoneCode':'zone_name'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import partridge as ptg\n",
    "\n",
    "def get_partridge_feed(zip_path, date):\n",
    "    service_ids_by_date = ptg.read_service_ids_by_date(zip_path)\n",
    "    service_ids = service_ids_by_date[date]\n",
    "\n",
    "    feed = ptg.feed(zip_path, view={\n",
    "        'trips.txt': {\n",
    "            'service_id': service_ids,\n",
    "        },\n",
    "    })\n",
    "    return feed\n",
    "    \n",
    "def to_timedelta(df):\n",
    "    '''\n",
    "    Turn time columns into timedelta dtype\n",
    "    '''\n",
    "    cols = ['arrival_time', 'departure_time']\n",
    "    numeric = df[cols].apply(pd.to_timedelta, unit='s')\n",
    "    df = df.copy()\n",
    "    df[cols] = numeric\n",
    "    return df\n",
    "\n",
    "def get_tidy_feed_df(feed, zones):\n",
    "    s = feed.stops\n",
    "    r = feed.routes\n",
    "    t = (feed.trips\n",
    "         .assign(route_id=lambda x: pd.Categorical(x['route_id'])))\n",
    "    f = (feed.stop_times[['trip_id', 'departure_time', 'arrival_time', 'stop_id', 'stop_sequence']]\n",
    "         .merge(s[['stop_id', 'stop_name', 'stop_lat', 'stop_lon', 'stop_code']], on='stop_id')\n",
    "         # Much faster joins and slices with Categorical dtypes\n",
    "         .merge(zones, how='left')\n",
    "         .assign(zone_name=lambda x: pd.Categorical(x['zone_name']))\n",
    "         .merge(t[['trip_id', 'route_id', 'direction_id']], on='trip_id')\n",
    "         .merge(r[['route_id', 'route_short_name', 'route_long_name']], on='route_id')\n",
    "         .assign(route_id=lambda x: pd.Categorical(x['route_id']))\n",
    "         .pipe(to_timedelta)\n",
    "        )\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOCAL_ZIP_PATH1 = 'data/gtfs_feeds/2018-05-01.zip' \n",
    "LOCAL_ZIP_PATH2 = 'data/gtfs_feeds/2018-05-02.zip' \n",
    "LOCAL_ZIP_PATH3 = 'data/gtfs_feeds/2018-04-25.zip' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LAG-BAOMER feeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feed2 = get_partridge_feed(LOCAL_ZIP_PATH2, datetime.date(2018, 5, 2))\n",
    "feed3 = get_partridge_feed(LOCAL_ZIP_PATH2, datetime.date(2018, 5, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last week's feeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "feed25 = get_partridge_feed(LOCAL_ZIP_PATH3, datetime.date(2018, 4, 25))\n",
    "feed26 = get_partridge_feed(LOCAL_ZIP_PATH3, datetime.date(2018, 4, 26))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Last Wed - This Wed): 0.030\n",
      "(Last Thu - This Wed): 0.074\n"
     ]
    }
   ],
   "source": [
    "print('(Last Wed - This Wed): %.3f' % ((feed25.trips.shape[0] - feed2.trips.shape[0])/feed25.trips.shape[0]))\n",
    "print('(Last Thu - This Wed): %.3f' % ((feed26.trips.shape[0] - feed3.trips.shape[0])/feed25.trips.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "f25 = get_tidy_feed_df(feed25, zones)\n",
    "f26 = get_tidy_feed_df(feed26, zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f2 = get_tidy_feed_df(feed2, zones)\n",
    "f3 = get_tidy_feed_df(feed3, zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "גוש דן                       4551\n",
       "סובב ירושלים                 2800\n",
       "סובב חיפה                    2416\n",
       "נתניה                        1610\n",
       "שרון                         1472\n",
       "נצרת                         1421\n",
       "נהריה                        1312\n",
       "גליל עליון ורמת הגולן        1294\n",
       "כרמיאל                       1039\n",
       "חריש ואדי ערה                1003\n",
       "עכו                           987\n",
       "אשדוד                         968\n",
       "רחובות                        927\n",
       "באר שבע                       842\n",
       "ראש העין שוהם                 824\n",
       "אשקלון                        775\n",
       "סובב כנרת ודרום רמת הגולן     700\n",
       "עפולה                         678\n",
       "יוקנעם- טבעון                 670\n",
       "חבל מודיעין                   668\n",
       "ערד דימונה                    619\n",
       "נתיבות שדרות                  561\n",
       "אזור בית שמש                  525\n",
       "אזור חדרה                     484\n",
       "רהט להבים                     460\n",
       "הרי ירושלים                   432\n",
       "קריית גת                      343\n",
       "בית שאן                       326\n",
       "השומרון                       318\n",
       "אופקים                        288\n",
       "זכרון                         241\n",
       "עומר חורה                     204\n",
       "ירוחם                         179\n",
       "גוש שילה ובקעה                161\n",
       "גוש עציון                     137\n",
       "בנימין                         91\n",
       "אזור קריית ארבע                68\n",
       "דרום הר חברון                  61\n",
       "בקעת ים המלח                   58\n",
       "Name: zone_name, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zones.zone_name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zone-specific difference ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "danwed = f25[f25.zone_name==\"גוש דן\"].trip_id.nunique() - f2[f2.zone_name==\"גוש דן\"].trip_id.nunique()\n",
    "danthu = f26[f26.zone_name==\"גוש דן\"].trip_id.nunique() - f3[f3.zone_name==\"גוש דן\"].trip_id.nunique()\n",
    "haiwed = f25[f25.zone_name==\"סובב חיפה\"].trip_id.nunique() - f2[f2.zone_name==\"סובב חיפה\"].trip_id.nunique()\n",
    "haithu = f26[f26.zone_name==\"סובב חיפה\"].trip_id.nunique() - f3[f3.zone_name==\"סובב חיפה\"].trip_id.nunique()\n",
    "akowed = f25[f25.zone_name==\"עכו\"].trip_id.nunique() - f2[f2.zone_name==\"עכו\"].trip_id.nunique()\n",
    "akothu = f26[f26.zone_name==\"עכו\"].trip_id.nunique() - f3[f3.zone_name==\"עכו\"].trip_id.nunique()\n",
    "nahwed = f25[f25.zone_name==\"נהריה\"].trip_id.nunique() - f2[f2.zone_name==\"נהריה\"].trip_id.nunique()\n",
    "nahthu = f26[f26.zone_name==\"נהריה\"].trip_id.nunique() - f3[f3.zone_name==\"נהריה\"].trip_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "netwed = f25[f25.zone_name==\"נתניה\"].trip_id.nunique() - f2[f2.zone_name==\"נתניה\"].trip_id.nunique()\n",
    "netthu = f26[f26.zone_name==\"נתניה\"].trip_id.nunique() - f3[f3.zone_name==\"נתניה\"].trip_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gush Dan Wed: 0.03\n",
      "Gush Dan Thu: 0.06\n",
      "Haifa Wed: 0.04\n",
      "Haifa Thu: 0.16\n",
      "Akko Wed: 0.16\n",
      "Akko Thu: 0.20\n",
      "Nahariya Wed: 0.13\n",
      "Nahariya Thu: 0.16\n",
      "Netanya Wed: 0.02\n",
      "Netanya Thu: 0.07\n"
     ]
    }
   ],
   "source": [
    "print('Gush Dan Wed: %.2f' % (danwed / f25[f25.zone_name==\"גוש דן\"].trip_id.nunique()))\n",
    "print('Gush Dan Thu: %.2f' % (danthu / f26[f26.zone_name==\"גוש דן\"].trip_id.nunique()))\n",
    "print('Haifa Wed: %.2f' % (haiwed / f25[f25.zone_name==\"סובב חיפה\"].trip_id.nunique()))\n",
    "print('Haifa Thu: %.2f' % (haithu / f26[f26.zone_name==\"סובב חיפה\"].trip_id.nunique()))\n",
    "print('Akko Wed: %.2f' % (akowed / f25[f25.zone_name==\"עכו\"].trip_id.nunique()))\n",
    "print('Akko Thu: %.2f' % (akothu / f26[f26.zone_name==\"עכו\"].trip_id.nunique()))\n",
    "print('Nahariya Wed: %.2f' % (nahwed / f25[f25.zone_name==\"נהריה\"].trip_id.nunique()))\n",
    "print('Nahariya Thu: %.2f' % (nahthu / f26[f26.zone_name==\"נהריה\"].trip_id.nunique()))\n",
    "print('Netanya Wed: %.2f' % (netwed / f25[f25.zone_name==\"נתניה\"].trip_id.nunique()))\n",
    "print('Netanya Thu: %.2f' % (netthu / f26[f26.zone_name==\"נתניה\"].trip_id.nunique()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Per route trip count difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feed26</th>\n",
       "      <th>feed3</th>\n",
       "      <th>dif</th>\n",
       "      <th>route_short_name</th>\n",
       "      <th>route_long_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15729</th>\n",
       "      <td>117.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2</td>\n",
       "      <td>אבא שאול/איסוף-ביתר עילית&lt;-&gt;דרך הרמ''ז/מפעל הש...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15730</th>\n",
       "      <td>116.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2</td>\n",
       "      <td>דרך הרמז/סדיגורא-ביתר עילית&lt;-&gt;אבא שאול/הורדה-ב...</td>\n",
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       "    <tr>\n",
       "      <th>10516</th>\n",
       "      <td>116.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5</td>\n",
       "      <td>בית כנסת/הרב שך-ביתר עילית&lt;-&gt;בית כנסת/הרב שך-ב...</td>\n",
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       "    <tr>\n",
       "      <th>10811</th>\n",
       "      <td>116.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>68</td>\n",
       "      <td>האוניברסיטה העברית הר הצופים-ירושלים&lt;-&gt;האונ' ה...</td>\n",
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       "    <tr>\n",
       "      <th>10944</th>\n",
       "      <td>93.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6</td>\n",
       "      <td>קדושת לוי/ברוק-ביתר עילית&lt;-&gt;בית כנסת/הרב שך-בי...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10943</th>\n",
       "      <td>92.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6</td>\n",
       "      <td>בית כנסת/הרב שך-ביתר עילית&lt;-&gt;קדושת לוי/ברוק-בי...</td>\n",
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       "    <tr>\n",
       "      <th>10252</th>\n",
       "      <td>68.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>59</td>\n",
       "      <td>ביטוח לאומי-ירושלים&lt;-&gt;וינוגרד/זווין-ירושלים-1#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>112.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3</td>\n",
       "      <td>ת. מרכזית נתניה-נתניה&lt;-&gt;ת. מרכזית נתניה-נתניה-3#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>95.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>62</td>\n",
       "      <td>בן ציון גליס/צבי ברגמן-פתח תקווה&lt;-&gt;מסוף שניידר...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12406</th>\n",
       "      <td>87.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>15</td>\n",
       "      <td>האומן/ברעם-ירושלים&lt;-&gt;בנייני האומה-ירושלים-2#</td>\n",
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       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>97.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>68</td>\n",
       "      <td>האונ' העברית קמפוס אדמונד י. ספרא/מסוף כפר ההי...</td>\n",
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       "    <tr>\n",
       "      <th>11322</th>\n",
       "      <td>100.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>115</td>\n",
       "      <td>ת. מרכזית חוף הכרמל/רציפים עירוני-חיפה&lt;-&gt;ת. מר...</td>\n",
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       "    <tr>\n",
       "      <th>11526</th>\n",
       "      <td>106.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6</td>\n",
       "      <td>מעלות החכמה-מודיעין עילית&lt;-&gt;אבי עזרי ז-מודיעין...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17089</th>\n",
       "      <td>65.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4 א</td>\n",
       "      <td>שוק/הכרם-אשקלון&lt;-&gt;מרכז אפרידר/ככר צפניה-אשקלון-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17090</th>\n",
       "      <td>64.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4 א</td>\n",
       "      <td>כיכר צפניה-אשקלון&lt;-&gt;מסוף-רמז-אשקלון-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4187</th>\n",
       "      <td>66.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2</td>\n",
       "      <td>המכללה האקדמית גליל מערבי/דרך המכללה-עכו&lt;-&gt;שלו...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>74.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18</td>\n",
       "      <td>חניון נווה שאנן-חיפה&lt;-&gt;ת. רכבת בת גלים-חיפה-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4189</th>\n",
       "      <td>63.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>שלום הגליל/בן עמי-עכו&lt;-&gt;המכללה האקדמית גליל מע...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10509</th>\n",
       "      <td>64.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>קדושת לוי/שלום רב-ביתר עילית&lt;-&gt;קדושת לוי/אוהל ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>78.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25</td>\n",
       "      <td>בית חולים בית רבקה-פתח תקווה&lt;-&gt;ת. מרכזית פ''ת/...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       feed26  feed3   dif route_short_name  \\\n",
       "15729   117.0   55.0  62.0                2   \n",
       "15730   116.0   55.0  61.0                2   \n",
       "10516   116.0   67.0  49.0                5   \n",
       "10811   116.0   69.0  47.0               68   \n",
       "10944    93.0   56.0  37.0                6   \n",
       "10943    92.0   56.0  36.0                6   \n",
       "10252    68.0   33.0  35.0               59   \n",
       "2117    112.0   77.0  35.0                3   \n",
       "1130     95.0   61.0  34.0               62   \n",
       "12406    87.0   54.0  33.0               15   \n",
       "10812    97.0   64.0  33.0               68   \n",
       "11322   100.0   68.0  32.0              115   \n",
       "11526   106.0   75.0  31.0                6   \n",
       "17089    65.0   34.0  31.0              4 א   \n",
       "17090    64.0   33.0  31.0              4 א   \n",
       "4187     66.0   35.0  31.0                2   \n",
       "3651     74.0   44.0  30.0               18   \n",
       "4189     63.0   33.0  30.0                2   \n",
       "10509    64.0   35.0  29.0                1   \n",
       "1105     78.0   49.0  29.0               25   \n",
       "\n",
       "                                         route_long_name  \n",
       "15729  אבא שאול/איסוף-ביתר עילית<->דרך הרמ''ז/מפעל הש...  \n",
       "15730  דרך הרמז/סדיגורא-ביתר עילית<->אבא שאול/הורדה-ב...  \n",
       "10516  בית כנסת/הרב שך-ביתר עילית<->בית כנסת/הרב שך-ב...  \n",
       "10811  האוניברסיטה העברית הר הצופים-ירושלים<->האונ' ה...  \n",
       "10944  קדושת לוי/ברוק-ביתר עילית<->בית כנסת/הרב שך-בי...  \n",
       "10943  בית כנסת/הרב שך-ביתר עילית<->קדושת לוי/ברוק-בי...  \n",
       "10252     ביטוח לאומי-ירושלים<->וינוגרד/זווין-ירושלים-1#  \n",
       "2117    ת. מרכזית נתניה-נתניה<->ת. מרכזית נתניה-נתניה-3#  \n",
       "1130   בן ציון גליס/צבי ברגמן-פתח תקווה<->מסוף שניידר...  \n",
       "12406       האומן/ברעם-ירושלים<->בנייני האומה-ירושלים-2#  \n",
       "10812  האונ' העברית קמפוס אדמונד י. ספרא/מסוף כפר ההי...  \n",
       "11322  ת. מרכזית חוף הכרמל/רציפים עירוני-חיפה<->ת. מר...  \n",
       "11526  מעלות החכמה-מודיעין עילית<->אבי עזרי ז-מודיעין...  \n",
       "17089  שוק/הכרם-אשקלון<->מרכז אפרידר/ככר צפניה-אשקלון-11  \n",
       "17090             כיכר צפניה-אשקלון<->מסוף-רמז-אשקלון-21  \n",
       "4187   המכללה האקדמית גליל מערבי/דרך המכללה-עכו<->שלו...  \n",
       "3651      חניון נווה שאנן-חיפה<->ת. רכבת בת גלים-חיפה-2#  \n",
       "4189   שלום הגליל/בן עמי-עכו<->המכללה האקדמית גליל מע...  \n",
       "10509  קדושת לוי/שלום רב-ביתר עילית<->קדושת לוי/אוהל ...  \n",
       "1105   בית חולים בית רבקה-פתח תקווה<->ת. מרכזית פ''ת/...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcmp = pd.concat([feed26.trips.route_id.value_counts(), feed3.trips.route_id.value_counts()], axis=1, keys=['feed26', 'feed3'])\n",
    "\n",
    "rcmp['dif'] = rcmp.feed26.subtract(rcmp.feed3)\n",
    "pd.concat((rcmp, feed26.routes.set_index('route_id')[['route_short_name', 'route_long_name']]), axis=1).sort_values(by='dif', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Per minute departure difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>departure_time</th>\n",
       "      <th>arrival_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_id</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                departure_time arrival_time\n",
       "trip_id                                    \n",
       "31908291_030518       00:00:00     00:11:34\n",
       "31134617_030518       00:00:00     00:11:54\n",
       "31973562_030518       00:00:00     00:12:17\n",
       "15347769_030518       00:05:00     00:12:37\n",
       "26424664_030518       00:05:00     00:17:00"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se = (feed3.stop_times.groupby(['trip_id'])\n",
    "     .agg({'departure_time': 'min',\n",
    "          'arrival_time': 'max'})\n",
    "     .pipe(to_timedelta)\n",
    "     .sort_values(['arrival_time', 'departure_time']))\n",
    "se.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>departure_time</th>\n",
       "      <th>arrival_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>00:12:17</td>\n",
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       "      <td>00:05:00</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                departure_time arrival_time\n",
       "trip_id                                    \n",
       "31908288_240418       00:00:00     00:11:34\n",
       "31134614_240418       00:00:00     00:11:54\n",
       "26125699_240418       00:00:00     00:12:17\n",
       "13660343_240418       00:05:00     00:12:37\n",
       "25359099_240418       00:05:00     00:17:00"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se2 = (feed26.stop_times.groupby(['trip_id'])\n",
    "     .agg({'departure_time': 'min',\n",
    "          'arrival_time': 'max'})\n",
    "     .pipe(to_timedelta)\n",
    "     .sort_values(['arrival_time', 'departure_time']))\n",
    "se2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1573b7cc780>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x154f1d99358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "departures = pd.Series(1, se.departure_time).resample('1Min').sum()\n",
    "arrivals =  pd.Series(1, se.arrival_time).resample('T').sum()\n",
    "onroad = pd.concat([pd.Series(1, se.departure_time),  # departed add 1\n",
    "                           pd.Series(-1, se.arrival_time)  # arrived substract 1\n",
    "                           ]).resample('1Min').sum().cumsum().ffill()\n",
    "df = (pd.concat([departures, arrivals, onroad], axis=1).reset_index()\n",
    "        .rename(columns={'index': 'time', 0:'departures', 1:'arrivals', 2:'onroad'}))\n",
    "idx = pd.DatetimeIndex(df.time+datetime.datetime(2018, 5, 1))\n",
    "\n",
    "(df.assign(time=idx)\n",
    " .set_index('time')\n",
    " .resample('1min').sum()\n",
    " .ewm(span=60)\n",
    " .mean()\n",
    " .plot())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15482eb28d0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x154befa8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "departures2 = pd.Series(1, se2.departure_time).resample('1Min').sum()\n",
    "arrivals2 =  pd.Series(1, se2.arrival_time).resample('T').sum()\n",
    "onroad2 = pd.concat([pd.Series(1, se2.departure_time),  # departed add 1\n",
    "                           pd.Series(-1, se2.arrival_time)  # arrived substract 1\n",
    "                           ]).resample('1Min').sum().cumsum().ffill()\n",
    "df2 = (pd.concat([departures2, arrivals2, onroad2], axis=1).reset_index()\n",
    "        .rename(columns={'index': 'time', 0:'departures', 1:'arrivals', 2:'onroad'}))\n",
    "#idx = pd.DatetimeIndex(df.time+datetime.datetime(2018, 5, 1))\n",
    "\n",
    "(df2.assign(time=idx)\n",
    " .set_index('time')\n",
    " .resample('1min').sum()\n",
    " .ewm(span=60)\n",
    " .mean()\n",
    " .plot())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks almost the same, lets line the departures up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Departures Per Minute (smoothed)')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1553951c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = pd.DatetimeIndex(departures.index+datetime.datetime(2018, 5, 1))\n",
    "\n",
    "df_m = (pd.concat([departures, departures2], axis=1).reset_index()\n",
    "        .rename(columns={'index': 'time', 1:'רבעש עובש', 0:'רמועב ג\"ל'}))\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "(df_m.assign(time=idx)\n",
    " .set_index('time')\n",
    " .resample('1min').sum()\n",
    " .ewm(span=120)\n",
    " .mean()\n",
    " .plot(ax=ax))\n",
    "\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Departures / Minute')\n",
    "ax.set_title('Departures Per Minute (smoothed)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GTFSTK\n",
    "\n",
    "Now for some statistical heavy-lifting I'll use [GTFSTK](https://github.com/mrcagney/gtfstk)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gtfstk as gt\n",
    "import shapely.geometry as sg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>file_name</th>\n",
       "      <th>file_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>agency.txt</td>\n",
       "      <td>2027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>calendar.txt</td>\n",
       "      <td>1670513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fare_attributes.txt</td>\n",
       "      <td>8635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fare_rules.txt</td>\n",
       "      <td>12264501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>routes.txt</td>\n",
       "      <td>975122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>shapes.txt</td>\n",
       "      <td>219980322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>stop_times.txt</td>\n",
       "      <td>972809367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>stops.txt</td>\n",
       "      <td>3948697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>translations.txt</td>\n",
       "      <td>4383402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>trips.txt</td>\n",
       "      <td>36304514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file_name  file_size\n",
       "0           agency.txt       2027\n",
       "1         calendar.txt    1670513\n",
       "2  fare_attributes.txt       8635\n",
       "3       fare_rules.txt   12264501\n",
       "4           routes.txt     975122\n",
       "5           shapes.txt  219980322\n",
       "6       stop_times.txt  972809367\n",
       "7            stops.txt    3948697\n",
       "8     translations.txt    4383402\n",
       "9            trips.txt   36304514"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List feed\n",
    "\n",
    "gt.list_gtfs(LOCAL_ZIP_PATH2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read and print feed\n",
    "\n",
    "feed = gt.read_gtfs(LOCAL_ZIP_PATH2, dist_units='m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indicator</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>agencies</td>\n",
       "      <td>[רכבת ישראל, אגד, אגד תעבורה, דן, ש.א.מ, נסיעו...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>timezone</td>\n",
       "      <td>Asia/Jerusalem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>start_date</td>\n",
       "      <td>20180501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>end_date</td>\n",
       "      <td>20180630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>num_routes</td>\n",
       "      <td>7761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>num_trips</td>\n",
       "      <td>512583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>num_stops</td>\n",
       "      <td>27795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>num_shapes</td>\n",
       "      <td>7658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sample_date</td>\n",
       "      <td>20180510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>num_routes_active_on_sample_date</td>\n",
       "      <td>6734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>num_trips_active_on_sample_date</td>\n",
       "      <td>90895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>num_stops_active_on_sample_date</td>\n",
       "      <td>27597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           indicator  \\\n",
       "0                           agencies   \n",
       "1                           timezone   \n",
       "2                         start_date   \n",
       "3                           end_date   \n",
       "4                         num_routes   \n",
       "5                          num_trips   \n",
       "6                          num_stops   \n",
       "7                         num_shapes   \n",
       "8                        sample_date   \n",
       "9   num_routes_active_on_sample_date   \n",
       "10   num_trips_active_on_sample_date   \n",
       "11   num_stops_active_on_sample_date   \n",
       "\n",
       "                                                value  \n",
       "0   [רכבת ישראל, אגד, אגד תעבורה, דן, ש.א.מ, נסיעו...  \n",
       "1                                      Asia/Jerusalem  \n",
       "2                                            20180501  \n",
       "3                                            20180630  \n",
       "4                                                7761  \n",
       "5                                              512583  \n",
       "6                                               27795  \n",
       "7                                                7658  \n",
       "8                                            20180510  \n",
       "9                                                6734  \n",
       "10                                              90895  \n",
       "11                                              27597  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>type</th>\n",
       "      <th>message</th>\n",
       "      <th>table</th>\n",
       "      <th>rows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>warning</td>\n",
       "      <td>Unrecognized column agency_id</td>\n",
       "      <td>fare_attributes</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>warning</td>\n",
       "      <td>Repeated pair (route_short_name, route_long_name)</td>\n",
       "      <td>routes</td>\n",
       "      <td>[15, 17, 18, 25, 26, 29, 30, 31, 33, 47, 50, 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>warning</td>\n",
       "      <td>Stop has no stop times</td>\n",
       "      <td>stops</td>\n",
       "      <td>[625, 1928, 2840, 2841, 3129, 3642, 3789, 4088...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      type                                            message  \\\n",
       "0  warning                      Unrecognized column agency_id   \n",
       "1  warning  Repeated pair (route_short_name, route_long_name)   \n",
       "2  warning                             Stop has no stop times   \n",
       "\n",
       "             table                                               rows  \n",
       "0  fare_attributes                                                 []  \n",
       "1           routes  [15, 17, 18, 25, 26, 29, 30, 31, 33, 47, 50, 7...  \n",
       "2            stops  [625, 1928, 2840, 2841, 3129, 3642, 3789, 4088...  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Validate\n",
    "\n",
    "feed.validate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>170643</th>\n",
       "      <th>170617</th>\n",
       "      <th>170644</th>\n",
       "      <th>170618</th>\n",
       "      <th>170619</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>trip_id</th>\n",
       "      <td>27727651_010518</td>\n",
       "      <td>27727521_010518</td>\n",
       "      <td>27727652_010518</td>\n",
       "      <td>27727522_010518</td>\n",
       "      <td>27727523_010518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>route_id</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>route_short_name</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>route_type</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direction_id</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shape_id</th>\n",
       "      <td>94990</td>\n",
       "      <td>94990</td>\n",
       "      <td>94990</td>\n",
       "      <td>94990</td>\n",
       "      <td>94990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_stops</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <td>05:05:00</td>\n",
       "      <td>05:10:00</td>\n",
       "      <td>05:30:00</td>\n",
       "      <td>05:40:00</td>\n",
       "      <td>06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_time</th>\n",
       "      <td>05:24:44</td>\n",
       "      <td>05:29:44</td>\n",
       "      <td>05:49:44</td>\n",
       "      <td>05:59:44</td>\n",
       "      <td>06:19:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_stop_id</th>\n",
       "      <td>38725</td>\n",
       "      <td>38725</td>\n",
       "      <td>38725</td>\n",
       "      <td>38725</td>\n",
       "      <td>38725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_stop_id</th>\n",
       "      <td>15745</td>\n",
       "      <td>15745</td>\n",
       "      <td>15745</td>\n",
       "      <td>15745</td>\n",
       "      <td>15745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_loop</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>0.328889</td>\n",
       "      <td>0.328889</td>\n",
       "      <td>0.328889</td>\n",
       "      <td>0.328889</td>\n",
       "      <td>0.328889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>7016</td>\n",
       "      <td>7016</td>\n",
       "      <td>7016</td>\n",
       "      <td>7016</td>\n",
       "      <td>7016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speed</th>\n",
       "      <td>21332.4</td>\n",
       "      <td>21332.4</td>\n",
       "      <td>21332.4</td>\n",
       "      <td>21332.4</td>\n",
       "      <td>21332.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           170643           170617           170644  \\\n",
       "trip_id           27727651_010518  27727521_010518  27727652_010518   \n",
       "route_id                        1                1                1   \n",
       "route_short_name                1                1                1   \n",
       "route_type                      3                3                3   \n",
       "direction_id                    0                0                0   \n",
       "shape_id                    94990            94990            94990   \n",
       "num_stops                      19               19               19   \n",
       "start_time               05:05:00         05:10:00         05:30:00   \n",
       "end_time                 05:24:44         05:29:44         05:49:44   \n",
       "start_stop_id               38725            38725            38725   \n",
       "end_stop_id                 15745            15745            15745   \n",
       "is_loop                         0                0                0   \n",
       "duration                 0.328889         0.328889         0.328889   \n",
       "distance                     7016             7016             7016   \n",
       "speed                     21332.4          21332.4          21332.4   \n",
       "\n",
       "                           170618           170619  \n",
       "trip_id           27727522_010518  27727523_010518  \n",
       "route_id                        1                1  \n",
       "route_short_name                1                1  \n",
       "route_type                      3                3  \n",
       "direction_id                    0                0  \n",
       "shape_id                    94990            94990  \n",
       "num_stops                      19               19  \n",
       "start_time               05:40:00         06:00:00  \n",
       "end_time                 05:59:44         06:19:44  \n",
       "start_stop_id               38725            38725  \n",
       "end_stop_id                 15745            15745  \n",
       "is_loop                         0                0  \n",
       "duration                 0.328889         0.328889  \n",
       "distance                     7016             7016  \n",
       "speed                     21332.4          21332.4  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute trip stats\n",
    "\n",
    "trip_stats = feed.compute_trip_stats()\n",
    "trip_stats.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5895941</th>\n",
       "      <th>5895942</th>\n",
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       "      <th>trip_id</th>\n",
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       "      <td>10021427_050518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_time</th>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:01:34</td>\n",
       "      <td>00:03:26</td>\n",
       "      <td>00:05:21</td>\n",
       "      <td>00:06:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>departure_time</th>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:01:34</td>\n",
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       "      <td>00:05:21</td>\n",
       "      <td>00:06:22</td>\n",
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       "    <tr>\n",
       "      <th>stop_id</th>\n",
       "      <td>36133</td>\n",
       "      <td>2356</td>\n",
       "      <td>3869</td>\n",
       "      <td>2357</td>\n",
       "      <td>3803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_sequence</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drop_off_type</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shape_dist_traveled</th>\n",
       "      <td>34.3139</td>\n",
       "      <td>502.771</td>\n",
       "      <td>1841.84</td>\n",
       "      <td>2696</td>\n",
       "      <td>3382.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             5895939          5895940          5895941  \\\n",
       "trip_id              10021427_050518  10021427_050518  10021427_050518   \n",
       "arrival_time                00:00:00         00:01:34         00:03:26   \n",
       "departure_time              00:00:00         00:01:34         00:03:26   \n",
       "stop_id                        36133             2356             3869   \n",
       "stop_sequence                      1                2                3   \n",
       "pickup_type                        0                0                0   \n",
       "drop_off_type                      1                1                1   \n",
       "shape_dist_traveled          34.3139          502.771          1841.84   \n",
       "\n",
       "                             5895942          5895943  \n",
       "trip_id              10021427_050518  10021427_050518  \n",
       "arrival_time                00:05:21         00:06:22  \n",
       "departure_time              00:05:21         00:06:22  \n",
       "stop_id                         2357             3803  \n",
       "stop_sequence                      4                5  \n",
       "pickup_type                        0                0  \n",
       "drop_off_type                      1                1  \n",
       "shape_dist_traveled             2696           3382.7  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add shape_dist_traveled column to stop times\n",
    "\n",
    "feed = feed.append_dist_to_stop_times(trip_stats)\n",
    "feed.stop_times.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose study dates\n",
    "\n",
    "#week = feed.get_first_week()\n",
    "#dates = [week[4], week[6]]  # First Friday and Sunday\n",
    "dates = ['20180502', '20180503']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <td>05:10:00</td>\n",
       "      <td>06:05:00</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>07:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_time</th>\n",
       "      <td>21:54:44</td>\n",
       "      <td>22:48:36</td>\n",
       "      <td>08:17:31</td>\n",
       "      <td>16:51:42</td>\n",
       "      <td>07:53:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_headway</th>\n",
       "      <td>65</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_headway</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_headway</th>\n",
       "      <td>37.8125</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peak_num_trips</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peak_start_time</th>\n",
       "      <td>05:10:00</td>\n",
       "      <td>15:55:00</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>07:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peak_end_time</th>\n",
       "      <td>05:29:44</td>\n",
       "      <td>16:03:36</td>\n",
       "      <td>08:17:31</td>\n",
       "      <td>13:21:42</td>\n",
       "      <td>07:53:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service_distance</th>\n",
       "      <td>182416</td>\n",
       "      <td>261100</td>\n",
       "      <td>6326</td>\n",
       "      <td>41880</td>\n",
       "      <td>7175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service_duration</th>\n",
       "      <td>8.55111</td>\n",
       "      <td>10.85</td>\n",
       "      <td>0.291944</td>\n",
       "      <td>1.80833</td>\n",
       "      <td>0.384167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service_speed</th>\n",
       "      <td>21332.4</td>\n",
       "      <td>24064.5</td>\n",
       "      <td>21668.5</td>\n",
       "      <td>23159.4</td>\n",
       "      <td>18676.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_trip_distance</th>\n",
       "      <td>7016</td>\n",
       "      <td>7460</td>\n",
       "      <td>6326</td>\n",
       "      <td>8376</td>\n",
       "      <td>7175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_trip_duration</th>\n",
       "      <td>0.328889</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.291944</td>\n",
       "      <td>0.361667</td>\n",
       "      <td>0.384167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>20180502</td>\n",
       "      <td>20180502</td>\n",
       "      <td>20180502</td>\n",
       "      <td>20180502</td>\n",
       "      <td>20180502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0         1         2         3         4\n",
       "route_id                   1        10     10000     10001     10002\n",
       "route_short_name           1         4        91        91        92\n",
       "route_type                 3         3         3         3         3\n",
       "num_trips                 26        35         1         5         1\n",
       "num_trip_starts           26        35         1         5         1\n",
       "num_trip_ends             26        35         1         5         1\n",
       "is_loop                    0         0         0         0         0\n",
       "is_bidirectional           0         0         0         0         0\n",
       "start_time          05:10:00  06:05:00  08:00:00  13:00:00  07:30:00\n",
       "end_time            21:54:44  22:48:36  08:17:31  16:51:42  07:53:03\n",
       "max_headway               65        50       NaN       125       NaN\n",
       "min_headway               20        10       NaN         0       NaN\n",
       "mean_headway         37.8125        28       NaN      52.5       NaN\n",
       "peak_num_trips             1         2         1         2         1\n",
       "peak_start_time     05:10:00  15:55:00  08:00:00  13:00:00  07:30:00\n",
       "peak_end_time       05:29:44  16:03:36  08:17:31  13:21:42  07:53:03\n",
       "service_distance      182416    261100      6326     41880      7175\n",
       "service_duration     8.55111     10.85  0.291944   1.80833  0.384167\n",
       "service_speed        21332.4   24064.5   21668.5   23159.4   18676.8\n",
       "mean_trip_distance      7016      7460      6326      8376      7175\n",
       "mean_trip_duration  0.328889      0.31  0.291944  0.361667  0.384167\n",
       "date                20180502  20180502  20180502  20180502  20180502"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "route_stats = feed.compute_route_stats(trip_stats, dates )\n",
    "route_stats.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>24064.516129</td>\n",
       "      <td>7460.0</td>\n",
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       "      <td>20180425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000</td>\n",
       "      <td>91</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>08:17:31</td>\n",
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       "      <td>6326.0</td>\n",
       "      <td>0.291944</td>\n",
       "      <td>21668.506185</td>\n",
       "      <td>6326.0</td>\n",
       "      <td>0.291944</td>\n",
       "      <td>20180425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10001</td>\n",
       "      <td>91</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>13:21:42</td>\n",
       "      <td>41880.0</td>\n",
       "      <td>1.808333</td>\n",
       "      <td>23159.447005</td>\n",
       "      <td>8376.0</td>\n",
       "      <td>0.361667</td>\n",
       "      <td>20180425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002</td>\n",
       "      <td>92</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>7175.0</td>\n",
       "      <td>0.384167</td>\n",
       "      <td>20180425</td>\n",
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       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  route_id route_short_name  route_type  num_trips  num_trip_starts  \\\n",
       "0        1                1         3.0       26.0             26.0   \n",
       "1       10                4         3.0       35.0             35.0   \n",
       "2    10000               91         3.0        1.0              1.0   \n",
       "3    10001               91         3.0        5.0              5.0   \n",
       "4    10002               92         3.0        1.0              1.0   \n",
       "\n",
       "   num_trip_ends  is_loop  is_bidirectional start_time  end_time    ...     \\\n",
       "0           26.0      0.0               0.0   05:10:00  21:54:44    ...      \n",
       "1           35.0      0.0               0.0   06:05:00  22:48:36    ...      \n",
       "2            1.0      0.0               0.0   08:00:00  08:17:31    ...      \n",
       "3            5.0      0.0               0.0   13:00:00  16:51:42    ...      \n",
       "4            1.0      0.0               0.0   07:30:00  07:53:03    ...      \n",
       "\n",
       "   mean_headway  peak_num_trips  peak_start_time  peak_end_time  \\\n",
       "0       37.8125             1.0         05:10:00       05:29:44   \n",
       "1       28.0000             2.0         15:55:00       16:03:36   \n",
       "2           NaN             1.0         08:00:00       08:17:31   \n",
       "3       52.5000             2.0         13:00:00       13:21:42   \n",
       "4           NaN             1.0         07:30:00       07:53:03   \n",
       "\n",
       "  service_distance service_duration  service_speed  mean_trip_distance  \\\n",
       "0         182416.0         8.551111   21332.432432              7016.0   \n",
       "1         261100.0        10.850000   24064.516129              7460.0   \n",
       "2           6326.0         0.291944   21668.506185              6326.0   \n",
       "3          41880.0         1.808333   23159.447005              8376.0   \n",
       "4           7175.0         0.384167   18676.789588              7175.0   \n",
       "\n",
       "   mean_trip_duration      date  \n",
       "0            0.328889  20180425  \n",
       "1            0.310000  20180425  \n",
       "2            0.291944  20180425  \n",
       "3            0.361667  20180425  \n",
       "4            0.384167  20180425  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feed_lw = gt.read_gtfs(LOCAL_ZIP_PATH3, dist_units='m')\n",
    "trip_stats_lw = feed_lw.compute_trip_stats()\n",
    "dates_lw = ['20180425', '20180426']\n",
    "route_stats_lw = feed_lw.compute_route_stats(trip_stats_lw, dates_lw )\n",
    "route_stats_lw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indicator</th>\n",
       "      <th>value_x</th>\n",
       "      <th>value_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>agencies</td>\n",
       "      <td>[רכבת ישראל, אגד, אגד תעבורה, דן, ש.א.מ, נסיעו...</td>\n",
       "      <td>[רכבת ישראל, אגד, אגד תעבורה, דן, ש.א.מ, נסיעו...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>timezone</td>\n",
       "      <td>Asia/Jerusalem</td>\n",
       "      <td>Asia/Jerusalem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>start_date</td>\n",
       "      <td>20180424</td>\n",
       "      <td>20180501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>end_date</td>\n",
       "      <td>20180623</td>\n",
       "      <td>20180630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>num_routes</td>\n",
       "      <td>7762</td>\n",
       "      <td>7761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>num_trips</td>\n",
       "      <td>485400</td>\n",
       "      <td>512583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>num_stops</td>\n",
       "      <td>27793</td>\n",
       "      <td>27795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>num_shapes</td>\n",
       "      <td>7628</td>\n",
       "      <td>7658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sample_date</td>\n",
       "      <td>20180503</td>\n",
       "      <td>20180510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>num_routes_active_on_sample_date</td>\n",
       "      <td>5656</td>\n",
       "      <td>6734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>num_trips_active_on_sample_date</td>\n",
       "      <td>83476</td>\n",
       "      <td>90895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>num_stops_active_on_sample_date</td>\n",
       "      <td>27080</td>\n",
       "      <td>27597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           indicator  \\\n",
       "0                           agencies   \n",
       "1                           timezone   \n",
       "2                         start_date   \n",
       "3                           end_date   \n",
       "4                         num_routes   \n",
       "5                          num_trips   \n",
       "6                          num_stops   \n",
       "7                         num_shapes   \n",
       "8                        sample_date   \n",
       "9   num_routes_active_on_sample_date   \n",
       "10   num_trips_active_on_sample_date   \n",
       "11   num_stops_active_on_sample_date   \n",
       "\n",
       "                                              value_x  \\\n",
       "0   [רכבת ישראל, אגד, אגד תעבורה, דן, ש.א.מ, נסיעו...   \n",
       "1                                      Asia/Jerusalem   \n",
       "2                                            20180424   \n",
       "3                                            20180623   \n",
       "4                                                7762   \n",
       "5                                              485400   \n",
       "6                                               27793   \n",
       "7                                                7628   \n",
       "8                                            20180503   \n",
       "9                                                5656   \n",
       "10                                              83476   \n",
       "11                                              27080   \n",
       "\n",
       "                                              value_y  \n",
       "0   [רכבת ישראל, אגד, אגד תעבורה, דן, ש.א.מ, נסיעו...  \n",
       "1                                      Asia/Jerusalem  \n",
       "2                                            20180501  \n",
       "3                                            20180630  \n",
       "4                                                7761  \n",
       "5                                              512583  \n",
       "6                                               27795  \n",
       "7                                                7658  \n",
       "8                                            20180510  \n",
       "9                                                6734  \n",
       "10                                              90895  \n",
       "11                                              27597  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feed_lw.describe().merge(feed.describe(), on='indicator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biggest Headway Diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">last_week</th>\n",
       "      <th colspan=\"2\" halign=\"left\">this_week</th>\n",
       "      <th colspan=\"2\" halign=\"left\">dif</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>route_short_name</th>\n",
       "      <th>num_trips</th>\n",
       "      <th>mean_headway</th>\n",
       "      <th>num_trips</th>\n",
       "      <th>mean_headway</th>\n",
       "      <th>headway</th>\n",
       "      <th>num_trips</th>\n",
       "      <th>route_long_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2184</th>\n",
       "      <td>22</td>\n",
       "      <td>34.0</td>\n",
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       "      <td>0.672970</td>\n",
       "      <td>0.441176</td>\n",
       "      <td>ת. מרכזית נתניה/רציפים-נתניה&lt;-&gt;ת. מרכזית נתניה...</td>\n",
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       "    <tr>\n",
       "      <th>10509</th>\n",
       "      <td>1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>קדושת לוי/שלום רב-ביתר עילית&lt;-&gt;קדושת לוי/אוהל ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3768</th>\n",
       "      <td>37</td>\n",
       "      <td>47.0</td>\n",
       "      <td>19.459459</td>\n",
       "      <td>22.0</td>\n",
       "      <td>53.076923</td>\n",
       "      <td>0.633373</td>\n",
       "      <td>0.468085</td>\n",
       "      <td>ת. רכבת בת גלים-חיפה&lt;-&gt;אוניברסיטה/רב תכליתי-נש...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>24.482759</td>\n",
       "      <td>15.0</td>\n",
       "      <td>64.090909</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>שכונת ראס אלח'אביה-מגאר&lt;-&gt;שכונת ראס אלח'אביה-מ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>24.642857</td>\n",
       "      <td>15.0</td>\n",
       "      <td>62.727273</td>\n",
       "      <td>0.607143</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>שכונת ראס אלח'אביה-מגאר&lt;-&gt;שכונת ראס אלח'אביה-מ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15729</th>\n",
       "      <td>2</td>\n",
       "      <td>117.0</td>\n",
       "      <td>8.650602</td>\n",
       "      <td>55.0</td>\n",
       "      <td>20.285714</td>\n",
       "      <td>0.573562</td>\n",
       "      <td>0.470085</td>\n",
       "      <td>אבא שאול/איסוף-ביתר עילית&lt;-&gt;דרך הרמ''ז/מפעל הש...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15730</th>\n",
       "      <td>2</td>\n",
       "      <td>116.0</td>\n",
       "      <td>8.756098</td>\n",
       "      <td>55.0</td>\n",
       "      <td>20.285714</td>\n",
       "      <td>0.568361</td>\n",
       "      <td>0.474138</td>\n",
       "      <td>דרך הרמז/סדיגורא-ביתר עילית&lt;-&gt;אבא שאול/הורדה-ב...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9623</th>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.550781</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>תרדיון/ביטוח לאומי-תרדיון&lt;-&gt;סחנין עיריה-סח'נין-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1575</th>\n",
       "      <td>332</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.588235</td>\n",
       "      <td>16.0</td>\n",
       "      <td>45.714286</td>\n",
       "      <td>0.549632</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>ת. רכבת חיפה מרכז-חיפה&lt;-&gt;מרכז מסחרי/שדרות מעלה...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17090</th>\n",
       "      <td>4 א</td>\n",
       "      <td>64.0</td>\n",
       "      <td>13.769231</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.541026</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>כיכר צפניה-אשקלון&lt;-&gt;מסוף-רמז-אשקלון-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17089</th>\n",
       "      <td>4 א</td>\n",
       "      <td>65.0</td>\n",
       "      <td>13.788462</td>\n",
       "      <td>34.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.540385</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>שוק/הכרם-אשקלון&lt;-&gt;מרכז אפרידר/ככר צפניה-אשקלון-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10252</th>\n",
       "      <td>59</td>\n",
       "      <td>68.0</td>\n",
       "      <td>16.744186</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.534884</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>ביטוח לאומי-ירושלים&lt;-&gt;וינוגרד/זווין-ירושלים-1#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9622</th>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>64.090909</td>\n",
       "      <td>0.531915</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>סחנין עיריה-סח'נין&lt;-&gt;תרדיון/מכללת וינגייט-תרדי...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14068</th>\n",
       "      <td>5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>48.928571</td>\n",
       "      <td>0.529927</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>סחנין עיריה-סח'נין&lt;-&gt;סחנין עיריה-סח'נין-3#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3593</th>\n",
       "      <td>9</td>\n",
       "      <td>37.0</td>\n",
       "      <td>28.541667</td>\n",
       "      <td>22.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.524306</td>\n",
       "      <td>0.594595</td>\n",
       "      <td>טרומפלדור/מרכז זיו-חיפה&lt;-&gt;טרומפלדור/מרכז זיו-ח...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16277</th>\n",
       "      <td>88</td>\n",
       "      <td>56.0</td>\n",
       "      <td>19.459459</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.517857</td>\n",
       "      <td>אליהו חכים/מאיר פיינשטיין-ירושלים&lt;-&gt;אליהו חכים...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16877</th>\n",
       "      <td>146</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>0.512195</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>קניון איילון-רמת גן&lt;-&gt;ת. מרכזית ת''א ק. 6/הורד...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20785</th>\n",
       "      <td>54</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>16.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.504132</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>בילאל/102926-נצרת&lt;-&gt;בית ספר תיכון שנלר-נצרת-1#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20391</th>\n",
       "      <td>53</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>16.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.504132</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>1006/אל פאח'ורה-נצרת&lt;-&gt;אלג'ליל/כניסה צפונית-נצ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20317</th>\n",
       "      <td>52</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>16.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.504132</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>7061/7074-נצרת&lt;-&gt;שוק נצרת עלית-נצרת עילית-1#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19671</th>\n",
       "      <td>500</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.782609</td>\n",
       "      <td>19.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.503623</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>ת. מרכזית המפרת/רציפים בינעירוני-חיפה&lt;-&gt;ת. מרכ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17428</th>\n",
       "      <td>63</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.363636</td>\n",
       "      <td>16.0</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.502165</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>מרכז מסחרי ביג-נצרת&lt;-&gt;ריינה/יציאה-ריינה-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17427</th>\n",
       "      <td>63</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>16.0</td>\n",
       "      <td>65.454545</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>ריינה א'/כניסה-ריינה&lt;-&gt;מרכז מסחרי ביג-נצרת-1#</td>\n",
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       "    <tr>\n",
       "      <th>3600</th>\n",
       "      <td>10</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>חסן שוקרי/הנביאים-חיפה&lt;-&gt;חסן שוקרי/הנביאים-חיפ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20392</th>\n",
       "      <td>53</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>17.0</td>\n",
       "      <td>65.454545</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>אלג'ליל/כניסה צפונית-נצרת&lt;-&gt;1006/אל פאח'ורה-נצ...</td>\n",
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       "    <tr>\n",
       "      <th>20786</th>\n",
       "      <td>54</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>17.0</td>\n",
       "      <td>65.454545</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>בית ספר תיכון שנלר-נצרת&lt;-&gt;בילאל/102926-נצרת-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16876</th>\n",
       "      <td>146</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>ת.מרכזית ת''א ק.6/רציפים-תל אביב יפו&lt;-&gt;קניון א...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20318</th>\n",
       "      <td>52</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>17.0</td>\n",
       "      <td>65.454545</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>שוק-נצרת עילית&lt;-&gt;7061/7074-נצרת-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17944</th>\n",
       "      <td>307</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>כאבול כניסה-כאבול&lt;-&gt;שדות-טמרה-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17943</th>\n",
       "      <td>307</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>טמרה כניסה-טמרה&lt;-&gt;כאבול ו'/יציאה-כאבול-1#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4187</th>\n",
       "      <td>2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>13.846154</td>\n",
       "      <td>35.0</td>\n",
       "      <td>27.230769</td>\n",
       "      <td>0.491525</td>\n",
       "      <td>0.530303</td>\n",
       "      <td>המכללה האקדמית גליל מערבי/דרך המכללה-עכו&lt;-&gt;שלו...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12428</th>\n",
       "      <td>77</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.604651</td>\n",
       "      <td>42.0</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>0.489088</td>\n",
       "      <td>0.656250</td>\n",
       "      <td>דרך דוד בנבנישתי-ירושלים&lt;-&gt;מסוף אגד/בר כוכבא-י...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4189</th>\n",
       "      <td>2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>14.117647</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.307692</td>\n",
       "      <td>0.483016</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>שלום הגליל/בן עמי-עכו&lt;-&gt;המכללה האקדמית גליל מע...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19672</th>\n",
       "      <td>500</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>ת. מרכזית ק''ש/רציפים-קרית שמונה&lt;-&gt;ת. מרכזית ה...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>332</td>\n",
       "      <td>41.0</td>\n",
       "      <td>22.419355</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.083333</td>\n",
       "      <td>0.467263</td>\n",
       "      <td>0.439024</td>\n",
       "      <td>מרכז מסחרי/שדרות מעלה יצחק-נצרת עילית&lt;-&gt;ת. רכב...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10811</th>\n",
       "      <td>68</td>\n",
       "      <td>116.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.594828</td>\n",
       "      <td>האוניברסיטה העברית הר הצופים-ירושלים&lt;-&gt;האונ' ה...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>10</td>\n",
       "      <td>43.0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.534884</td>\n",
       "      <td>אזור תעשיה ב-נצרת עילית&lt;-&gt;אזור תעשיה ב-נצרת עי...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12374</th>\n",
       "      <td>77</td>\n",
       "      <td>66.0</td>\n",
       "      <td>16.090909</td>\n",
       "      <td>43.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.463636</td>\n",
       "      <td>0.651515</td>\n",
       "      <td>מסוף אגד/בר כוכבא-ירושלים&lt;-&gt;מסוף אגד/בנבנישתי-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15352</th>\n",
       "      <td>46</td>\n",
       "      <td>41.0</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>41.875000</td>\n",
       "      <td>0.462687</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>שכונת נמסאווי-נצרת&lt;-&gt;גן טכנולוגי/כניסה ראשית-נ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9624</th>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>25.961538</td>\n",
       "      <td>23.0</td>\n",
       "      <td>48.214286</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>סחנין עיריה-סח'נין&lt;-&gt;סחנין עיריה-סח'נין-3#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10516</th>\n",
       "      <td>5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>8.814815</td>\n",
       "      <td>67.0</td>\n",
       "      <td>16.363636</td>\n",
       "      <td>0.461317</td>\n",
       "      <td>0.577586</td>\n",
       "      <td>בית כנסת/הרב שך-ביתר עילית&lt;-&gt;בית כנסת/הרב שך-ב...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13516</th>\n",
       "      <td>74</td>\n",
       "      <td>40.0</td>\n",
       "      <td>22.580645</td>\n",
       "      <td>23.0</td>\n",
       "      <td>41.250000</td>\n",
       "      <td>0.452590</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>ת. מרכזית חדרה/רציפים-חדרה&lt;-&gt;רוטשילד/מנחם ציונ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15353</th>\n",
       "      <td>46</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.225806</td>\n",
       "      <td>22.0</td>\n",
       "      <td>42.352941</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>גן טכנולוגי-נצרת&lt;-&gt;שכונת נמסאוי-נצרת-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10944</th>\n",
       "      <td>6</td>\n",
       "      <td>93.0</td>\n",
       "      <td>10.833333</td>\n",
       "      <td>56.0</td>\n",
       "      <td>19.722222</td>\n",
       "      <td>0.450704</td>\n",
       "      <td>0.602151</td>\n",
       "      <td>קדושת לוי/ברוק-ביתר עילית&lt;-&gt;בית כנסת/הרב שך-בי...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21175</th>\n",
       "      <td>15</td>\n",
       "      <td>57.0</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>36.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>ששת הימים/הנשיאים-מגדל העמק&lt;-&gt;מסוף מגדל העמק-מ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17855</th>\n",
       "      <td>5</td>\n",
       "      <td>57.0</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>36.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>מסוף מגדל העמק/רציפים-מגדל העמק&lt;-&gt;ששת הימים/הנ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10943</th>\n",
       "      <td>6</td>\n",
       "      <td>92.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>19.722222</td>\n",
       "      <td>0.442254</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>בית כנסת/הרב שך-ביתר עילית&lt;-&gt;קדושת לוי/ברוק-בי...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12406</th>\n",
       "      <td>15</td>\n",
       "      <td>87.0</td>\n",
       "      <td>11.428571</td>\n",
       "      <td>54.0</td>\n",
       "      <td>20.285714</td>\n",
       "      <td>0.436620</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>האומן/ברעם-ירושלים&lt;-&gt;בנייני האומה-ירושלים-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12024</th>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.652174</td>\n",
       "      <td>19.0</td>\n",
       "      <td>54.230769</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>ת. מרכזית נהריה/רציפים-נהריה&lt;-&gt;ת. מרכזית נהריה...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13517</th>\n",
       "      <td>75</td>\n",
       "      <td>39.0</td>\n",
       "      <td>20.645161</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.470588</td>\n",
       "      <td>0.433923</td>\n",
       "      <td>0.589744</td>\n",
       "      <td>שד. הנשיא וייצמן/הרצוג-אור עקיבא&lt;-&gt;אחד העם/סמס...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20691</th>\n",
       "      <td>4</td>\n",
       "      <td>56.0</td>\n",
       "      <td>17.073171</td>\n",
       "      <td>36.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.430894</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>ששת הימים/הנשיאים-מגדל העמק&lt;-&gt;מסוף מגדל העמק-מ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20690</th>\n",
       "      <td>4</td>\n",
       "      <td>55.0</td>\n",
       "      <td>17.142857</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>מסוף מגדל העמק/רציפים-מגדל העמק&lt;-&gt;ששת הימים/הנ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10946</th>\n",
       "      <td>3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>0.426087</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>ת. מרכזית לוד/רציפים-לוד&lt;-&gt;ת. מרכזית לוד/רציפי...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12405</th>\n",
       "      <td>15</td>\n",
       "      <td>85.0</td>\n",
       "      <td>11.639344</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.418033</td>\n",
       "      <td>0.670588</td>\n",
       "      <td>ביטוח לאומי-ירושלים&lt;-&gt;האומן/ברעם-ירושלים-1#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19760</th>\n",
       "      <td>21</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.791667</td>\n",
       "      <td>23.0</td>\n",
       "      <td>51.071429</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>רשי/צלאח-קרית אתא&lt;-&gt;ת. רכבת חוצות המפרץ-חיפה-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>62</td>\n",
       "      <td>95.0</td>\n",
       "      <td>10.588235</td>\n",
       "      <td>61.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.642105</td>\n",
       "      <td>בן ציון גליס/צבי ברגמן-פתח תקווה&lt;-&gt;מסוף שניידר...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>53</td>\n",
       "      <td>84.0</td>\n",
       "      <td>11.612903</td>\n",
       "      <td>58.0</td>\n",
       "      <td>19.459459</td>\n",
       "      <td>0.403226</td>\n",
       "      <td>0.690476</td>\n",
       "      <td>ת. מרכזית פ''ת/רציפים אזורי-פתח תקווה&lt;-&gt;רכבת ק...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14556</th>\n",
       "      <td>3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>39.0</td>\n",
       "      <td>24.642857</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.609375</td>\n",
       "      <td>דרך הארבעה/העצמאות-עכו&lt;-&gt;שלום הגליל/הרב רייפמן...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14555</th>\n",
       "      <td>13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>37.0</td>\n",
       "      <td>24.642857</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578125</td>\n",
       "      <td>שלום הגליל/בן עמי-עכו&lt;-&gt;דרך הארבעה/העצמאות-עכו-1#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19759</th>\n",
       "      <td>21</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>49.285714</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>ת. רכבת חוצות המפרץ-חיפה&lt;-&gt;רשי/צלאח-קרית אתא-1#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3748</th>\n",
       "      <td>34</td>\n",
       "      <td>49.0</td>\n",
       "      <td>18.421053</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.385965</td>\n",
       "      <td>0.653061</td>\n",
       "      <td>הנשיא/מרכז פנורמה-חיפה&lt;-&gt;הנשיא/מרכז פנורמה-חיפ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>33</td>\n",
       "      <td>49.0</td>\n",
       "      <td>18.461538</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.632653</td>\n",
       "      <td>הנשיא/מרכז פנורמה-חיפה&lt;-&gt;הנשיא/מרכז פנורמה-חיפ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>68</td>\n",
       "      <td>97.0</td>\n",
       "      <td>9.230769</td>\n",
       "      <td>64.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.659794</td>\n",
       "      <td>האונ' העברית קמפוס אדמונד י. ספרא/מסוף כפר ההי...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>23.0</td>\n",
       "      <td>47.142857</td>\n",
       "      <td>0.381313</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>הסחלב/קהילת יהדות צרפת-מעלות תרשיחא&lt;-&gt;בית כנסת...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13367</th>\n",
       "      <td>22</td>\n",
       "      <td>59.0</td>\n",
       "      <td>17.142857</td>\n",
       "      <td>41.0</td>\n",
       "      <td>27.692308</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>מר. קריות איסוף-קרית מוצקין&lt;-&gt;מינץ/הרצוג-קרית ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10510</th>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>7.797101</td>\n",
       "      <td>45.0</td>\n",
       "      <td>12.558140</td>\n",
       "      <td>0.379120</td>\n",
       "      <td>0.633803</td>\n",
       "      <td>חניון עילית 1/תומכי תמימים-ביתר עילית&lt;-&gt;חניון ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16711</th>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>18.684211</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.377193</td>\n",
       "      <td>0.673077</td>\n",
       "      <td>דרך הטייסים/דרך בוזגלו-אופקים&lt;-&gt;ת. רכבת אופקים...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11322</th>\n",
       "      <td>115</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.355263</td>\n",
       "      <td>68.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.376316</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>ת. מרכזית חוף הכרמל/רציפים עירוני-חיפה&lt;-&gt;ת. מר...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3559</th>\n",
       "      <td>4</td>\n",
       "      <td>34.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>אבן גבירול/ביל''ו-חיפה&lt;-&gt;אבן גבירול/ביל''ו-חיפ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>53א</td>\n",
       "      <td>84.0</td>\n",
       "      <td>12.118644</td>\n",
       "      <td>58.0</td>\n",
       "      <td>19.324324</td>\n",
       "      <td>0.372881</td>\n",
       "      <td>0.690476</td>\n",
       "      <td>ת. רכבת קרית אריה-פתח תקווה&lt;-&gt;ת. מרכזית פ''ת/ה...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16706</th>\n",
       "      <td>3</td>\n",
       "      <td>51.0</td>\n",
       "      <td>18.918919</td>\n",
       "      <td>36.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.369369</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>דרך הטייסים/דרך בוזגלו-אופקים&lt;-&gt;ת. רכבת אופקים...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>3</td>\n",
       "      <td>112.0</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>77.0</td>\n",
       "      <td>12.421053</td>\n",
       "      <td>0.369350</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>ת. מרכזית נתניה-נתניה&lt;-&gt;ת. מרכזית נתניה-נתניה-3#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2143</th>\n",
       "      <td>8א</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.775000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>24.960000</td>\n",
       "      <td>0.367989</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>ת. מרכזית נתניה-נתניה&lt;-&gt;ת. מרכזית נתניה-נתניה-3ה</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4444</th>\n",
       "      <td>271</td>\n",
       "      <td>57.0</td>\n",
       "      <td>11.428571</td>\n",
       "      <td>39.0</td>\n",
       "      <td>18.064516</td>\n",
       "      <td>0.367347</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>ת. מרכזית נהריה/רציפים-נהריה&lt;-&gt;ת. מרכזית המפרץ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16066</th>\n",
       "      <td>419</td>\n",
       "      <td>45.0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.736842</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>מסוף אגד/הר חוצבים-ירושלים&lt;-&gt;יחזקאל הנביא/חבקו...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15620</th>\n",
       "      <td>9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>17.875000</td>\n",
       "      <td>58.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.361607</td>\n",
       "      <td>1.017544</td>\n",
       "      <td>הנשיא וייצמן/שז''ר-טבריה&lt;-&gt;ת. מרכזית טבריה/הור...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19579</th>\n",
       "      <td>5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>19.189189</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.360360</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>בית חולים אסף הרופא/מכון אונקולוגיה-באר יעקב&lt;-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19580</th>\n",
       "      <td>5</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.189189</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.360360</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>מסוף קניון הזהב-ראשון לציון&lt;-&gt;בית חולים אסף הר...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11071</th>\n",
       "      <td>1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>12.909091</td>\n",
       "      <td>58.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.354545</td>\n",
       "      <td>0.743590</td>\n",
       "      <td>אבי עזרי א-מודיעין עילית&lt;-&gt;מעלות החכמה-מודיעין...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3662</th>\n",
       "      <td>20</td>\n",
       "      <td>64.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>23.225806</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.703125</td>\n",
       "      <td>ערד/סובה-חיפה&lt;-&gt;ערד/סובה-חיפה-3#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11072</th>\n",
       "      <td>1</td>\n",
       "      <td>77.0</td>\n",
       "      <td>12.962963</td>\n",
       "      <td>58.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.351852</td>\n",
       "      <td>0.753247</td>\n",
       "      <td>מעלות החכמה-מודיעין עילית&lt;-&gt;אבי עזרי ז-מודיעין...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20665</th>\n",
       "      <td>59</td>\n",
       "      <td>52.0</td>\n",
       "      <td>19.459459</td>\n",
       "      <td>37.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>0.711538</td>\n",
       "      <td>דרך מנחם בגין-מסוף-מודיעין מכבים רעות&lt;-&gt;עיריית...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4202</th>\n",
       "      <td>3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>15.434783</td>\n",
       "      <td>39.0</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.709091</td>\n",
       "      <td>מסוף אגד ביג כרמיאל-כרמיאל&lt;-&gt;ביג כרמיאל/מרכז מ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10647</th>\n",
       "      <td>134</td>\n",
       "      <td>35.0</td>\n",
       "      <td>26.730769</td>\n",
       "      <td>26.0</td>\n",
       "      <td>40.882353</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>בנייני האומה/שז''ר-ירושלים&lt;-&gt;מרכז מסחרי/אגן הא...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12392</th>\n",
       "      <td>78</td>\n",
       "      <td>72.0</td>\n",
       "      <td>14.875000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>22.741935</td>\n",
       "      <td>0.345922</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>רב החובל/אדם-ירושלים&lt;-&gt;בנייני האומה-ירושלים-1#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12403</th>\n",
       "      <td>6</td>\n",
       "      <td>73.0</td>\n",
       "      <td>13.090909</td>\n",
       "      <td>53.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.345455</td>\n",
       "      <td>0.726027</td>\n",
       "      <td>מסוף אגד/כנפי נשרים-ירושלים&lt;-&gt;מסוף אגד/בנבנישת...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13389</th>\n",
       "      <td>112</td>\n",
       "      <td>67.0</td>\n",
       "      <td>13.557692</td>\n",
       "      <td>47.0</td>\n",
       "      <td>20.571429</td>\n",
       "      <td>0.340946</td>\n",
       "      <td>0.701493</td>\n",
       "      <td>השיטה/מגרשי טניס-נשר&lt;-&gt;ת. מרכזית חוף הכרמל/הור...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2141</th>\n",
       "      <td>8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>15.853659</td>\n",
       "      <td>37.0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.339431</td>\n",
       "      <td>0.711538</td>\n",
       "      <td>ת. מרכזית נתניה-נתניה&lt;-&gt;ת. מרכזית נתניה-נתניה-3#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12394</th>\n",
       "      <td>78</td>\n",
       "      <td>77.0</td>\n",
       "      <td>13.226415</td>\n",
       "      <td>56.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.338679</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>בנייני האומה/שדרות שז''ר-ירושלים&lt;-&gt;רב החובל/אד...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>62</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.076923</td>\n",
       "      <td>62.0</td>\n",
       "      <td>16.744186</td>\n",
       "      <td>0.338462</td>\n",
       "      <td>0.688889</td>\n",
       "      <td>מסוף שניידר-פתח תקווה&lt;-&gt;בן ציון גליס/צבי ברגמן...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>37</td>\n",
       "      <td>73.0</td>\n",
       "      <td>13.692308</td>\n",
       "      <td>54.0</td>\n",
       "      <td>20.588235</td>\n",
       "      <td>0.334945</td>\n",
       "      <td>0.739726</td>\n",
       "      <td>ת.מרכזית ת''א ק.7/רציפים-תל אביב יפו&lt;-&gt;מסוף יה...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>6</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.857143</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.285714</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>אלכרום/יציאה-נצרת&lt;-&gt;בית ספר נעמ''ת-נצרת-2#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>39.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.735849</td>\n",
       "      <td>ת. מרכזית עפולה/רציפים-עפולה&lt;-&gt;צוקית/טבלן-עפול...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>2</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>39.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>כפר דחי-דחי&lt;-&gt;ת. מרכזית עפולה/הורדה-עפולה-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16712</th>\n",
       "      <td>3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>איצטדיון כדורגל/וייצמן-נתיבות&lt;-&gt;ת. רכבת נתיבות...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16713</th>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>ת. רכבת נתיבות-נתיבות&lt;-&gt;בעלי המלאכה/שד.וייצמן-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11108</th>\n",
       "      <td>9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>שח''ל/הרצוג-ירושלים&lt;-&gt;בנייני האומה-ירושלים-2ד</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16709</th>\n",
       "      <td>5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>דרך הטייסים/דרך בוזגלו-אופקים&lt;-&gt;ת. רכבת אופקים...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11107</th>\n",
       "      <td>9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>ביטוח לאומי-ירושלים&lt;-&gt;שח''ל/הרצוג-ירושלים-1ד</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16704</th>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>ת. רכבת אופקים-אופקים&lt;-&gt;דרך הטייסים/דרך בוזגלו...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             last_week                        this_week               \\\n",
       "      route_short_name num_trips mean_headway num_trips mean_headway   \n",
       "2184                22      34.0    20.275862      15.0    62.000000   \n",
       "10509                1      64.0     6.600000      35.0    20.000000   \n",
       "3768                37      47.0    19.459459      22.0    53.076923   \n",
       "1397                 4      40.0    24.482759      15.0    64.090909   \n",
       "1398                 3      40.0    24.642857      15.0    62.727273   \n",
       "15729                2     117.0     8.650602      55.0    20.285714   \n",
       "15730                2     116.0     8.756098      55.0    20.285714   \n",
       "9623                 1      37.0    28.750000      16.0    64.000000   \n",
       "1575               332      40.0    20.588235      16.0    45.714286   \n",
       "17090              4 א      64.0    13.769231      33.0    30.000000   \n",
       "17089              4 א      65.0    13.788462      34.0    30.000000   \n",
       "10252               59      68.0    16.744186      33.0    36.000000   \n",
       "9622                 1      37.0    30.000000      16.0    64.090909   \n",
       "14068                5      40.0    23.000000      18.0    48.928571   \n",
       "3593                 9      37.0    28.541667      22.0    60.000000   \n",
       "16277               88      56.0    19.459459      29.0    40.000000   \n",
       "16877              146      31.0    30.000000      16.0    61.500000   \n",
       "20785               54      31.0    32.727273      16.0    66.000000   \n",
       "20391               53      33.0    32.727273      16.0    66.000000   \n",
       "20317               52      34.0    32.727273      16.0    66.000000   \n",
       "19671              500      31.0    29.782609      19.0    60.000000   \n",
       "17428               63      32.0    31.363636      16.0    63.000000   \n",
       "17427               63      32.0    32.727273      16.0    65.454545   \n",
       "3600                10      32.0    30.000000      17.0    60.000000   \n",
       "20392               53      33.0    32.727273      17.0    65.454545   \n",
       "20786               54      31.0    32.727273      17.0    65.454545   \n",
       "16876              146      32.0    30.000000      17.0    60.000000   \n",
       "20318               52      34.0    32.727273      17.0    65.454545   \n",
       "17944              307      35.0    30.000000      18.0    60.000000   \n",
       "17943              307      35.0    30.000000      18.0    60.000000   \n",
       "4187                 2      66.0    13.846154      35.0    27.230769   \n",
       "12428               77      64.0    16.604651      42.0    32.500000   \n",
       "4189                 2      63.0    14.117647      33.0    27.307692   \n",
       "19672              500      31.0    30.000000      19.0    56.666667   \n",
       "1569               332      41.0    22.419355      18.0    42.083333   \n",
       "10811               68     116.0     8.000000      69.0    15.000000   \n",
       "1275                10      43.0    24.000000      23.0    45.000000   \n",
       "12374               77      66.0    16.090909      43.0    30.000000   \n",
       "15352               46      41.0    22.500000      22.0    41.875000   \n",
       "9624                 2      45.0    25.961538      23.0    48.214286   \n",
       "10516                5     116.0     8.814815      67.0    16.363636   \n",
       "13516               74      40.0    22.580645      23.0    41.250000   \n",
       "15353               46      40.0    23.225806      22.0    42.352941   \n",
       "10944                6      93.0    10.833333      56.0    19.722222   \n",
       "21175               15      57.0    16.666667      36.0    30.000000   \n",
       "17855                5      57.0    16.666667      36.0    30.000000   \n",
       "10943                6      92.0    11.000000      56.0    19.722222   \n",
       "12406               15      87.0    11.428571      54.0    20.285714   \n",
       "12024                4      31.0    30.652174      19.0    54.230769   \n",
       "13517               75      39.0    20.645161      23.0    36.470588   \n",
       "20691                4      56.0    17.073171      36.0    30.000000   \n",
       "20690                4      55.0    17.142857      35.0    30.000000   \n",
       "10946                3      31.0    33.000000      25.0    57.500000   \n",
       "12405               15      85.0    11.639344      57.0    20.000000   \n",
       "19760               21      35.0    29.791667      23.0    51.071429   \n",
       "1130                62      95.0    10.588235      61.0    18.000000   \n",
       "1125                53      84.0    11.612903      58.0    19.459459   \n",
       "14556                3      64.0    15.000000      39.0    24.642857   \n",
       "14555               13      64.0    15.000000      37.0    24.642857   \n",
       "19759               21      33.0    30.000000      22.0    49.285714   \n",
       "3748                34      49.0    18.421053      32.0    30.000000   \n",
       "3743                33      49.0    18.461538      31.0    30.000000   \n",
       "10812               68      97.0     9.230769      64.0    15.000000   \n",
       "496                  1      34.0    29.166667      23.0    47.142857   \n",
       "13367               22      59.0    17.142857      41.0    27.692308   \n",
       "10510                1      71.0     7.797101      45.0    12.558140   \n",
       "16711                2      52.0    18.684211      35.0    30.000000   \n",
       "11322              115     100.0     9.355263      68.0    15.000000   \n",
       "3559                 4      34.0    25.000000      23.0    40.000000   \n",
       "1126               53א      84.0    12.118644      58.0    19.324324   \n",
       "16706                3      51.0    18.918919      36.0    30.000000   \n",
       "2117                 3     112.0     7.833333      77.0    12.421053   \n",
       "2143                8א      50.0    15.775000      34.0    24.960000   \n",
       "4444               271      57.0    11.428571      39.0    18.064516   \n",
       "16066              419      45.0    22.000000      30.0    34.736842   \n",
       "15620                9      57.0    17.875000      58.0    28.000000   \n",
       "19579                5      50.0    19.189189      35.0    30.000000   \n",
       "19580                5      49.0    19.189189      35.0    30.000000   \n",
       "11071                1      78.0    12.909091      58.0    20.000000   \n",
       "3662                20      64.0    15.000000      45.0    23.225806   \n",
       "11072                1      77.0    12.962963      58.0    20.000000   \n",
       "20665               59      52.0    19.459459      37.0    30.000000   \n",
       "4202                 3      55.0    15.434783      39.0    23.666667   \n",
       "10647              134      35.0    26.730769      26.0    40.882353   \n",
       "12392               78      72.0    14.875000      50.0    22.741935   \n",
       "12403                6      73.0    13.090909      53.0    20.000000   \n",
       "13389              112      67.0    13.557692      47.0    20.571429   \n",
       "2141                 8      52.0    15.853659      37.0    24.000000   \n",
       "12394               78      77.0    13.226415      56.0    20.000000   \n",
       "1128                62      90.0    11.076923      62.0    16.744186   \n",
       "965                 37      73.0    13.692308      54.0    20.588235   \n",
       "1256                 6      31.0    32.857143      20.0    49.285714   \n",
       "850                  3      53.0    20.000000      39.0    30.000000   \n",
       "849                  2      57.0    20.000000      39.0    30.000000   \n",
       "16712                3      48.0    20.000000      34.0    30.000000   \n",
       "16713                3      46.0    20.000000      34.0    30.000000   \n",
       "11108                9      54.0    20.000000      36.0    30.000000   \n",
       "16709                5      46.0    20.000000      34.0    30.000000   \n",
       "11107                9      54.0    20.000000      36.0    30.000000   \n",
       "16704                3      46.0    20.000000      34.0    30.000000   \n",
       "\n",
       "            dif                                                            a  \n",
       "        headway num_trips                                    route_long_name  \n",
       "2184   0.672970  0.441176  ת. מרכזית נתניה/רציפים-נתניה<->ת. מרכזית נתניה...  \n",
       "10509  0.670000  0.546875  קדושת לוי/שלום רב-ביתר עילית<->קדושת לוי/אוהל ...  \n",
       "3768   0.633373  0.468085  ת. רכבת בת גלים-חיפה<->אוניברסיטה/רב תכליתי-נש...  \n",
       "1397   0.618000  0.375000  שכונת ראס אלח'אביה-מגאר<->שכונת ראס אלח'אביה-מ...  \n",
       "1398   0.607143  0.375000  שכונת ראס אלח'אביה-מגאר<->שכונת ראס אלח'אביה-מ...  \n",
       "15729  0.573562  0.470085  אבא שאול/איסוף-ביתר עילית<->דרך הרמ''ז/מפעל הש...  \n",
       "15730  0.568361  0.474138  דרך הרמז/סדיגורא-ביתר עילית<->אבא שאול/הורדה-ב...  \n",
       "9623   0.550781  0.432432  תרדיון/ביטוח לאומי-תרדיון<->סחנין עיריה-סח'נין-2#  \n",
       "1575   0.549632  0.400000  ת. רכבת חיפה מרכז-חיפה<->מרכז מסחרי/שדרות מעלה...  \n",
       "17090  0.541026  0.515625             כיכר צפניה-אשקלון<->מסוף-רמז-אשקלון-21  \n",
       "17089  0.540385  0.523077  שוק/הכרם-אשקלון<->מרכז אפרידר/ככר צפניה-אשקלון-11  \n",
       "10252  0.534884  0.485294     ביטוח לאומי-ירושלים<->וינוגרד/זווין-ירושלים-1#  \n",
       "9622   0.531915  0.432432  סחנין עיריה-סח'נין<->תרדיון/מכללת וינגייט-תרדי...  \n",
       "14068  0.529927  0.450000         סחנין עיריה-סח'נין<->סחנין עיריה-סח'נין-3#  \n",
       "3593   0.524306  0.594595  טרומפלדור/מרכז זיו-חיפה<->טרומפלדור/מרכז זיו-ח...  \n",
       "16277  0.513514  0.517857  אליהו חכים/מאיר פיינשטיין-ירושלים<->אליהו חכים...  \n",
       "16877  0.512195  0.516129  קניון איילון-רמת גן<->ת. מרכזית ת''א ק. 6/הורד...  \n",
       "20785  0.504132  0.516129     בילאל/102926-נצרת<->בית ספר תיכון שנלר-נצרת-1#  \n",
       "20391  0.504132  0.484848  1006/אל פאח'ורה-נצרת<->אלג'ליל/כניסה צפונית-נצ...  \n",
       "20317  0.504132  0.470588       7061/7074-נצרת<->שוק נצרת עלית-נצרת עילית-1#  \n",
       "19671  0.503623  0.612903  ת. מרכזית המפרת/רציפים בינעירוני-חיפה<->ת. מרכ...  \n",
       "17428  0.502165  0.500000         מרכז מסחרי ביג-נצרת<->ריינה/יציאה-ריינה-2#  \n",
       "17427  0.500000  0.500000      ריינה א'/כניסה-ריינה<->מרכז מסחרי ביג-נצרת-1#  \n",
       "3600   0.500000  0.531250  חסן שוקרי/הנביאים-חיפה<->חסן שוקרי/הנביאים-חיפ...  \n",
       "20392  0.500000  0.515152  אלג'ליל/כניסה צפונית-נצרת<->1006/אל פאח'ורה-נצ...  \n",
       "20786  0.500000  0.548387     בית ספר תיכון שנלר-נצרת<->בילאל/102926-נצרת-2#  \n",
       "16876  0.500000  0.531250  ת.מרכזית ת''א ק.6/רציפים-תל אביב יפו<->קניון א...  \n",
       "20318  0.500000  0.500000                 שוק-נצרת עילית<->7061/7074-נצרת-2#  \n",
       "17944  0.500000  0.514286                   כאבול כניסה-כאבול<->שדות-טמרה-2#  \n",
       "17943  0.500000  0.514286          טמרה כניסה-טמרה<->כאבול ו'/יציאה-כאבול-1#  \n",
       "4187   0.491525  0.530303  המכללה האקדמית גליל מערבי/דרך המכללה-עכו<->שלו...  \n",
       "12428  0.489088  0.656250  דרך דוד בנבנישתי-ירושלים<->מסוף אגד/בר כוכבא-י...  \n",
       "4189   0.483016  0.523810  שלום הגליל/בן עמי-עכו<->המכללה האקדמית גליל מע...  \n",
       "19672  0.470588  0.612903  ת. מרכזית ק''ש/רציפים-קרית שמונה<->ת. מרכזית ה...  \n",
       "1569   0.467263  0.439024  מרכז מסחרי/שדרות מעלה יצחק-נצרת עילית<->ת. רכב...  \n",
       "10811  0.466667  0.594828  האוניברסיטה העברית הר הצופים-ירושלים<->האונ' ה...  \n",
       "1275   0.466667  0.534884  אזור תעשיה ב-נצרת עילית<->אזור תעשיה ב-נצרת עי...  \n",
       "12374  0.463636  0.651515  מסוף אגד/בר כוכבא-ירושלים<->מסוף אגד/בנבנישתי-...  \n",
       "15352  0.462687  0.536585  שכונת נמסאווי-נצרת<->גן טכנולוגי/כניסה ראשית-נ...  \n",
       "9624   0.461538  0.511111         סחנין עיריה-סח'נין<->סחנין עיריה-סח'נין-3#  \n",
       "10516  0.461317  0.577586  בית כנסת/הרב שך-ביתר עילית<->בית כנסת/הרב שך-ב...  \n",
       "13516  0.452590  0.575000  ת. מרכזית חדרה/רציפים-חדרה<->רוטשילד/מנחם ציונ...  \n",
       "15353  0.451613  0.550000            גן טכנולוגי-נצרת<->שכונת נמסאוי-נצרת-2#  \n",
       "10944  0.450704  0.602151  קדושת לוי/ברוק-ביתר עילית<->בית כנסת/הרב שך-בי...  \n",
       "21175  0.444444  0.631579  ששת הימים/הנשיאים-מגדל העמק<->מסוף מגדל העמק-מ...  \n",
       "17855  0.444444  0.631579  מסוף מגדל העמק/רציפים-מגדל העמק<->ששת הימים/הנ...  \n",
       "10943  0.442254  0.608696  בית כנסת/הרב שך-ביתר עילית<->קדושת לוי/ברוק-בי...  \n",
       "12406  0.436620  0.620690       האומן/ברעם-ירושלים<->בנייני האומה-ירושלים-2#  \n",
       "12024  0.434783  0.612903  ת. מרכזית נהריה/רציפים-נהריה<->ת. מרכזית נהריה...  \n",
       "13517  0.433923  0.589744  שד. הנשיא וייצמן/הרצוג-אור עקיבא<->אחד העם/סמס...  \n",
       "20691  0.430894  0.642857  ששת הימים/הנשיאים-מגדל העמק<->מסוף מגדל העמק-מ...  \n",
       "20690  0.428571  0.636364  מסוף מגדל העמק/רציפים-מגדל העמק<->ששת הימים/הנ...  \n",
       "10946  0.426087  0.806452  ת. מרכזית לוד/רציפים-לוד<->ת. מרכזית לוד/רציפי...  \n",
       "12405  0.418033  0.670588        ביטוח לאומי-ירושלים<->האומן/ברעם-ירושלים-1#  \n",
       "19760  0.416667  0.657143    רשי/צלאח-קרית אתא<->ת. רכבת חוצות המפרץ-חיפה-2#  \n",
       "1130   0.411765  0.642105  בן ציון גליס/צבי ברגמן-פתח תקווה<->מסוף שניידר...  \n",
       "1125   0.403226  0.690476  ת. מרכזית פ''ת/רציפים אזורי-פתח תקווה<->רכבת ק...  \n",
       "14556  0.391304  0.609375  דרך הארבעה/העצמאות-עכו<->שלום הגליל/הרב רייפמן...  \n",
       "14555  0.391304  0.578125  שלום הגליל/בן עמי-עכו<->דרך הארבעה/העצמאות-עכו-1#  \n",
       "19759  0.391304  0.666667    ת. רכבת חוצות המפרץ-חיפה<->רשי/צלאח-קרית אתא-1#  \n",
       "3748   0.385965  0.653061  הנשיא/מרכז פנורמה-חיפה<->הנשיא/מרכז פנורמה-חיפ...  \n",
       "3743   0.384615  0.632653  הנשיא/מרכז פנורמה-חיפה<->הנשיא/מרכז פנורמה-חיפ...  \n",
       "10812  0.384615  0.659794  האונ' העברית קמפוס אדמונד י. ספרא/מסוף כפר ההי...  \n",
       "496    0.381313  0.676471  הסחלב/קהילת יהדות צרפת-מעלות תרשיחא<->בית כנסת...  \n",
       "13367  0.380952  0.694915  מר. קריות איסוף-קרית מוצקין<->מינץ/הרצוג-קרית ...  \n",
       "10510  0.379120  0.633803  חניון עילית 1/תומכי תמימים-ביתר עילית<->חניון ...  \n",
       "16711  0.377193  0.673077  דרך הטייסים/דרך בוזגלו-אופקים<->ת. רכבת אופקים...  \n",
       "11322  0.376316  0.680000  ת. מרכזית חוף הכרמל/רציפים עירוני-חיפה<->ת. מר...  \n",
       "3559   0.375000  0.676471  אבן גבירול/ביל''ו-חיפה<->אבן גבירול/ביל''ו-חיפ...  \n",
       "1126   0.372881  0.690476  ת. רכבת קרית אריה-פתח תקווה<->ת. מרכזית פ''ת/ה...  \n",
       "16706  0.369369  0.705882  דרך הטייסים/דרך בוזגלו-אופקים<->ת. רכבת אופקים...  \n",
       "2117   0.369350  0.687500   ת. מרכזית נתניה-נתניה<->ת. מרכזית נתניה-נתניה-3#  \n",
       "2143   0.367989  0.680000   ת. מרכזית נתניה-נתניה<->ת. מרכזית נתניה-נתניה-3ה  \n",
       "4444   0.367347  0.684211  ת. מרכזית נהריה/רציפים-נהריה<->ת. מרכזית המפרץ...  \n",
       "16066  0.366667  0.666667  מסוף אגד/הר חוצבים-ירושלים<->יחזקאל הנביא/חבקו...  \n",
       "15620  0.361607  1.017544  הנשיא וייצמן/שז''ר-טבריה<->ת. מרכזית טבריה/הור...  \n",
       "19579  0.360360  0.700000  בית חולים אסף הרופא/מכון אונקולוגיה-באר יעקב<-...  \n",
       "19580  0.360360  0.714286  מסוף קניון הזהב-ראשון לציון<->בית חולים אסף הר...  \n",
       "11071  0.354545  0.743590  אבי עזרי א-מודיעין עילית<->מעלות החכמה-מודיעין...  \n",
       "3662   0.354167  0.703125                   ערד/סובה-חיפה<->ערד/סובה-חיפה-3#  \n",
       "11072  0.351852  0.753247  מעלות החכמה-מודיעין עילית<->אבי עזרי ז-מודיעין...  \n",
       "20665  0.351351  0.711538  דרך מנחם בגין-מסוף-מודיעין מכבים רעות<->עיריית...  \n",
       "4202   0.347826  0.709091  מסוף אגד ביג כרמיאל-כרמיאל<->ביג כרמיאל/מרכז מ...  \n",
       "10647  0.346154  0.742857  בנייני האומה/שז''ר-ירושלים<->מרכז מסחרי/אגן הא...  \n",
       "12392  0.345922  0.694444     רב החובל/אדם-ירושלים<->בנייני האומה-ירושלים-1#  \n",
       "12403  0.345455  0.726027  מסוף אגד/כנפי נשרים-ירושלים<->מסוף אגד/בנבנישת...  \n",
       "13389  0.340946  0.701493  השיטה/מגרשי טניס-נשר<->ת. מרכזית חוף הכרמל/הור...  \n",
       "2141   0.339431  0.711538   ת. מרכזית נתניה-נתניה<->ת. מרכזית נתניה-נתניה-3#  \n",
       "12394  0.338679  0.727273  בנייני האומה/שדרות שז''ר-ירושלים<->רב החובל/אד...  \n",
       "1128   0.338462  0.688889  מסוף שניידר-פתח תקווה<->בן ציון גליס/צבי ברגמן...  \n",
       "965    0.334945  0.739726  ת.מרכזית ת''א ק.7/רציפים-תל אביב יפו<->מסוף יה...  \n",
       "1256   0.333333  0.645161         אלכרום/יציאה-נצרת<->בית ספר נעמ''ת-נצרת-2#  \n",
       "850    0.333333  0.735849  ת. מרכזית עפולה/רציפים-עפולה<->צוקית/טבלן-עפול...  \n",
       "849    0.333333  0.684211       כפר דחי-דחי<->ת. מרכזית עפולה/הורדה-עפולה-20  \n",
       "16712  0.333333  0.708333  איצטדיון כדורגל/וייצמן-נתיבות<->ת. רכבת נתיבות...  \n",
       "16713  0.333333  0.739130  ת. רכבת נתיבות-נתיבות<->בעלי המלאכה/שד.וייצמן-...  \n",
       "11108  0.333333  0.666667      שח''ל/הרצוג-ירושלים<->בנייני האומה-ירושלים-2ד  \n",
       "16709  0.333333  0.739130  דרך הטייסים/דרך בוזגלו-אופקים<->ת. רכבת אופקים...  \n",
       "11107  0.333333  0.666667       ביטוח לאומי-ירושלים<->שח''ל/הרצוג-ירושלים-1ד  \n",
       "16704  0.333333  0.739130  ת. רכבת אופקים-אופקים<->דרך הטייסים/דרך בוזגלו...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conc = (pd.concat((route_stats_lw[(route_stats_lw.date=='20180426') & (route_stats_lw.num_trips>30)].set_index('route_id')[['route_short_name', 'num_trips', 'mean_headway']], \n",
    "route_stats[route_stats.date=='20180503'].set_index('route_id')[['num_trips', 'mean_headway']]), axis=1, keys=['last_week', 'this_week'])\n",
    " .dropna()  \n",
    ")\n",
    "conc[('dif', 'headway')] = 1 - (conc.iloc[:, 2])/conc.iloc[:, 4]\n",
    "conc[('dif', 'num_trips')] = (conc.iloc[:, 3])/conc.iloc[:, 1]\n",
    "\n",
    "with pd.option_context('display.max_rows', 10000, 'display.max_columns', 10):\n",
    "     display(conc.sort_values(by=[('dif', 'headway')], ascending=False).head(100).merge((pd.concat([feed26.routes[['route_id', 'route_long_name']].set_index('route_id')], \n",
    "                                                                                                  axis=1, keys=['a'])), \n",
    "                                                                                       how='left', left_index=True, right_index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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