Project-Documentation/Covid-Transit-Mapping/Join Transit Headway Data to Route Geography.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd, geopandas as gp, matplotlib.pyplot as plt, numpy as np\n",
    "import json\n",
    "import fiona\n",
    "from arcgis.gis import GIS\n",
    "from arcgis.features import GeoAccessor, GeoSeriesAccessor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Path to project directory\n",
    "\n",
    "[COVID-19 Transit Mapping for Blue Ribbon Task For Box Directory](https://mtcdrive.box.com/s/1xsylbgcyad7i5pmpe6vqzt0vfnvue5h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "box_data_dir = '../../../../../Box/DataViz Projects/Spatial Analysis and Mapping/COVID-19 Transit Mapping for Blue Ribbon Task Force/Data/Databases'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read ArcGIS Online password from txt file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_file_contents(filename):\n",
    "    \"\"\" Given a filename,\n",
    "        return the contents of that file\n",
    "    \"\"\"\n",
    "    try:\n",
    "        with open(filename, 'r') as f:\n",
    "            # It's assumed our file contains a single line,\n",
    "            # with our API key\n",
    "            return f.read().strip()\n",
    "    except FileNotFoundError:\n",
    "        print(\"'%s' file not found\" % filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mData\u001b[m\u001b[m\r\n",
      "Join Transit Headway Data to Route Geography.ipynb\r\n",
      "README.md\r\n",
      "\u001b[34mScripts\u001b[m\u001b[m\r\n",
      "Write RTD to CSV.ipynb\r\n",
      "agolpassword.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pass_txt = 'agolpassword.txt'\n",
    "password = get_file_contents(pass_txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conect to ArcGIS Online"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gis = GIS(\"https://www.arcgis.com\", \"content_MTC\", password)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test connection by returning user content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Item title:\"Major Rail Stations\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"emp_chg_per_acre_1_8_mile_10_cart\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"Sonoma County Opportunity Sites (2018)\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"Bay Area Ethnic Minority Concentration Tracts 2016 ACS 5 Year Estimates (May 2018)\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"CalEPA CalEnviroScreen 3.0 (2017)\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"Change_in_Jobs_per_Acre_2010_2040___1_4_Milre\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"RTD_SF_SC_AC_Source_Geoms_Draft1\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"CTCAC HCD Opportunity Areas (2017) - FMMP Built-Up Land\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"Public_Lands_Suitable_For_Housing_Development_2018_WFL1\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"Draft_High_Frequency_Bus_Rtoues_RTD2017\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"pda_2015_businesses_employment_2016\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"2019 TIP Investment Analysis Polygons\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"D_Fire_Hazards (Draft)\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"BCDC ART Indicators (2017)\" type:Feature Layer Collection owner:content_MTC>,\n",
       " <Item title:\"CAL FIRE - Historic Wildfire Perimeters (1950-2014)\" type:Feature Layer Collection owner:content_MTC>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_content = gis.content.search(query=\"owner:\" + gis.users.me.username, \n",
    "                                item_type=\"Feature Layer\", \n",
    "                                max_items=15)\n",
    "\n",
    "my_content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List ESRI transit analysis geodatabase contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../../../../../Box/DataViz Projects/Spatial Analysis and Mapping/COVID-19 Transit Mapping for Blue Ribbon Task Force/Data/Databases'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mCSV_results\u001b[m\u001b[m                   \u001b[34mRegional Transit Database.gdb\u001b[m\u001b[m\r\n",
      "\u001b[34mKearey_June2020_CSV_Results\u001b[m\u001b[m\r\n"
     ]
    }
   ],
   "source": [
    "!ls '../../../../../Box/DataViz Projects/Spatial Analysis and Mapping/COVID-19 Transit Mapping for Blue Ribbon Task Force/Data/Databases'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mn_nw',\n",
       " 'mn_jc',\n",
       " 'RG_Stops',\n",
       " 'RG_Routes',\n",
       " 'RV_Routes',\n",
       " 'RV_Stops',\n",
       " 'SA_Routes',\n",
       " 'SA_Stops',\n",
       " 'SB_Routes',\n",
       " 'SB_Stops',\n",
       " 'SC_Routes',\n",
       " 'SC_Stops',\n",
       " 'SF_Routes',\n",
       " 'SF_Stops',\n",
       " 'SI_Routes',\n",
       " 'SI_Stops',\n",
       " 'SM_Routes',\n",
       " 'SM_Stops',\n",
       " 'SO_Routes',\n",
       " 'SO_Stops',\n",
       " 'SR_Routes',\n",
       " 'SR_Stops',\n",
       " 'SS_Routes',\n",
       " 'SS_Stops',\n",
       " 'ST_Routes',\n",
       " 'ST_Stops',\n",
       " 'TD_Routes',\n",
       " 'TD_Stops',\n",
       " 'TriDelta_Routes',\n",
       " 'TriDelta_Stops',\n",
       " 'UC_Routes',\n",
       " 'UC_Stops',\n",
       " 'VC_Routes',\n",
       " 'VC_Stops',\n",
       " 'VN_Routes',\n",
       " 'VN_Stops',\n",
       " 'WC_Routes',\n",
       " 'WC_Stops',\n",
       " 'WH_Routes',\n",
       " 'WH_Stops',\n",
       " 'PE_Stops',\n",
       " 'AC_Routes',\n",
       " 'AC_Stops',\n",
       " 'AM_Routes',\n",
       " 'AM_Stops',\n",
       " 'BA_Routes',\n",
       " 'BA_Stops',\n",
       " 'CC_Routes',\n",
       " 'CC_Stops',\n",
       " 'CE_Routes',\n",
       " 'CE_Stops',\n",
       " 'CM_Routes',\n",
       " 'CM_Stops',\n",
       " 'CT_Routes',\n",
       " 'CT_Stops',\n",
       " 'DE_Routes',\n",
       " 'DE_Stops',\n",
       " 'EM_Routes',\n",
       " 'EM_Stops',\n",
       " 'FS_Routes',\n",
       " 'FS_Stops',\n",
       " 'GF_Routes',\n",
       " 'GF_Stops',\n",
       " 'GG_Routes',\n",
       " 'GG_Stops',\n",
       " 'MA_Routes',\n",
       " 'MA_Stops',\n",
       " 'PE_Routes',\n",
       " 'route_shape_repaired_6_22_20',\n",
       " 'weekday_all_day_route_frequency_6_30_20',\n",
       " 'weekday_am_route_frequency_6_30_20',\n",
       " 'weekday_pm_route_frequency_6_30_20']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiona.listlayers(os.path.join(box_data_dir,'Regional Transit Database.gdb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read pretty transit route data from ESRI geodatabase\n",
    "\n",
    "ACE train route was replaced with correct shape which was provided in the historical feed from January. Route shapes were generated previously using ESRI GTFS tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretty_routes = gp.read_file(os.path.join(box_data_dir,'Regional Transit Database.gdb'), \n",
    "                             driver='FileGDB',\n",
    "                             layer='route_shape_repaired_6_22_20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pretty_routes.plot(figsize=(10,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4326'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_routes.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 3069 entries, 0 to 3068\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   shape_id          3068 non-null   object  \n",
      " 1   route_id          1500 non-null   object  \n",
      " 2   agency_id         1500 non-null   object  \n",
      " 3   route_short_name  1499 non-null   object  \n",
      " 4   route_long_name   1486 non-null   object  \n",
      " 5   route_desc        159 non-null    object  \n",
      " 6   route_type        1499 non-null   object  \n",
      " 7   route_url         929 non-null    object  \n",
      " 8   route_color       1270 non-null   object  \n",
      " 9   route_text_color  1139 non-null   object  \n",
      " 10  route_sort_order  1499 non-null   object  \n",
      " 11  route_type_text   1500 non-null   object  \n",
      " 12  agency_name       1500 non-null   object  \n",
      " 13  Shape_Length      3069 non-null   float64 \n",
      " 14  geometry          3057 non-null   geometry\n",
      "dtypes: float64(1), geometry(1), object(13)\n",
      "memory usage: 359.8+ KB\n"
     ]
    }
   ],
   "source": [
    "pretty_routes.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter out SFO airport shuttle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Tri Delta Transit', 'AC Transit', None,\n",
       "       'Capitol Corridor Joint Powers Authority',\n",
       "       'Bay Area Rapid Transit', 'County Connection',\n",
       "       'Commute.org Shuttles', 'Caltrain', 'Dumbarton Express Consortium',\n",
       "       'Emery Go-Round', 'Fairfield and Suisun Transit',\n",
       "       'Golden Gate Ferry', 'Golden Gate Transit', 'Marin Transit',\n",
       "       'Petaluma Transit', 'Rio Vista Delta Breeze',\n",
       "       'Sonoma Marin Area Rail Transit', 'San Francisco Bay Ferry', 'VTA',\n",
       "       'San Francisco Municipal Transportation Agency',\n",
       "       'San Francisco International Airport', 'SamTrans',\n",
       "       'Sonoma County Transit', 'Santa Rosa CityBus',\n",
       "       'City of South San Francisco', 'SolTrans', 'Tideline Water Taxi',\n",
       "       'Union City Transit', 'Vacaville City Coach', 'VINE Transit',\n",
       "       'WestCat (Western Contra Costa)',\n",
       "       'Livermore Amador Valley Transit Authority',\n",
       "       'Altamont Corridor Express'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_routes['agency_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiLineString    3050\n",
       "Name: geometry, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_routes.groupby(pretty_routes['geometry'].type)['geometry'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    732\u001b[0m                 \u001b[0mrender_links\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m             )\n\u001b[0;32m--> 734\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnotebook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36mto_html\u001b[0;34m(self, buf, encoding, classes, notebook, border)\u001b[0m\n\u001b[1;32m    981\u001b[0m         \u001b[0mKlass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNotebookFormatter\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnotebook\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mHTMLFormatter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m         return Klass(self, classes=classes, border=border).get_result(\n\u001b[0;32m--> 983\u001b[0;31m             \u001b[0mbuf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    984\u001b[0m         )\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self, buf, encoding)\u001b[0m\n\u001b[1;32m    519\u001b[0m         \"\"\"\n\u001b[1;32m    520\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/html.py\u001b[0m in \u001b[0;36mwrite_result\u001b[0;34m(self, buf)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrite_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIO\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m         \u001b[0mbuffer_put_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_write_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/html.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    610\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"<div>\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    611\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_style\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 612\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    613\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"</div>\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0melements\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/html.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_write_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_show_dimensions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/html.py\u001b[0m in \u001b[0;36m_write_table\u001b[0;34m(self, indent)\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_write_header\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindent\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindent_delta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_write_body\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindent\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindent_delta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"</table>\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/html.py\u001b[0m in \u001b[0;36m_write_body\u001b[0;34m(self, indent)\u001b[0m\n\u001b[1;32m    383\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_write_body\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"<tbody>\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m         \u001b[0mfmt_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_formatted_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;31m# write values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/html.py\u001b[0m in \u001b[0;36m_get_formatted_values\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_formatted_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format_col\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mncols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_columns_formatted_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/html.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_formatted_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format_col\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mncols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_columns_formatted_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36m_format_col\u001b[0;34m(self, i)\u001b[0m\n\u001b[1;32m    952\u001b[0m             \u001b[0mna_rep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mna_rep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    953\u001b[0m             \u001b[0mspace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcol_space\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 954\u001b[0;31m             \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    955\u001b[0m         )\n\u001b[1;32m    956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36mformat_array\u001b[0;34m(values, formatter, float_format, na_rep, digits, space, justify, decimal, leading_space)\u001b[0m\n\u001b[1;32m   1170\u001b[0m     )\n\u001b[1;32m   1171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1172\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfmt_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1203\u001b[0;31m         \u001b[0mfmt_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format_strings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1204\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_make_fixed_width\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfmt_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjustify\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/io/formats/format.py\u001b[0m in \u001b[0;36m_format_strings\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1481\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1483\u001b[0;31m         \u001b[0mformatter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_formatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mboxed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1485\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/geopandas/array.py\u001b[0m in \u001b[0;36m_formatter\u001b[0;34m(self, boxed)\u001b[0m\n\u001b[1;32m   1009\u001b[0m                 \u001b[0;31m# dummy heuristic based on 10 first geometries that should\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1010\u001b[0m                 \u001b[0;31m# work in most cases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1011\u001b[0;31m                 \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_bounds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1012\u001b[0m                 if (\n\u001b[1;32m   1013\u001b[0m                     \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m180\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mxmin\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m180\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/geopandas/array.py\u001b[0m in \u001b[0;36mtotal_bounds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    750\u001b[0m         return np.array(\n\u001b[1;32m    751\u001b[0m             (\n\u001b[0;32m--> 752\u001b[0;31m                 \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# minx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    753\u001b[0m                 \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# miny\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m                 \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# maxx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Empty GeoDataFrame\n",
       "Columns: [shape_id, route_id, agency_id, route_short_name, route_long_name, route_desc, route_type, route_url, route_color, route_text_color, route_sort_order, route_type_text, agency_name, Shape_Length, geometry]\n",
       "Index: []"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_routes[pretty_routes['geometry'].type == None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretty_routes = pretty_routes[pretty_routes['agency_name'] != 'San Francisco International Airport']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join routes with headway csvs for the following transit service time periods January 2020:\n",
    "\n",
    "- AM Peak (6:00-10:00AM)\n",
    "- Midday (10:00AM-3:00PM)\n",
    "- PM Peak (3:00-7:00PM)\n",
    "- Latenight (7:00PM-6:00AM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m2020-01\u001b[m\u001b[m \u001b[34m2020-06\u001b[m\u001b[m\r\n"
     ]
    }
   ],
   "source": [
    "!ls '../../../../../Box/DataViz Projects/Spatial Analysis and Mapping/COVID-19 Transit Mapping for Blue Ribbon Task Force/Data/Databases/CSV_results'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../../../../../Box/DataViz Projects/Spatial Analysis and Mapping/COVID-19 Transit Mapping for Blue Ribbon Task Force/Data/Databases'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read January CSVs to dataframe\n",
    "\n",
    "jan_all_day = pd.read_csv(os.path.join(box_data_dir,'CSV_results/2020-01/all_6am_6am_blue_ribbon_calcs.csv'))\n",
    "jan_am_peak = pd.read_csv(os.path.join(box_data_dir,'CSV_results/2020-01/am_blue_ribbon_calcs.csv'))\n",
    "jan_mid_day = pd.read_csv(os.path.join(box_data_dir,'CSV_results/2020-01/mid_blue_ribbon_calcs.csv'))\n",
    "jan_pm_peak = pd.read_csv(os.path.join(box_data_dir,'CSV_results/2020-01/pm_blue_ribbon_calcs.csv'))\n",
    "jan_late_day = pd.read_csv(os.path.join(box_data_dir,'CSV_results/2020-01/late_blue_ribbon_calcs.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 359 entries, 0 to 358\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   agency_id         359 non-null    object \n",
      " 1   agency_name       359 non-null    object \n",
      " 2   route_id          359 non-null    object \n",
      " 3   route_short_name  359 non-null    object \n",
      " 4   route_long_name   359 non-null    object \n",
      " 5   route_type        359 non-null    int64  \n",
      " 6   route_url         359 non-null    object \n",
      " 7   total_departures  359 non-null    int64  \n",
      " 8   median_headways   359 non-null    float64\n",
      " 9   mean_headways     359 non-null    float64\n",
      " 10  st_dev_headways   333 non-null    float64\n",
      " 11  stop_count        359 non-null    int64  \n",
      " 12  service_class     359 non-null    object \n",
      "dtypes: float64(3), int64(3), object(7)\n",
      "memory usage: 36.6+ KB\n"
     ]
    }
   ],
   "source": [
    "jan_all_day.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>agency_id</th>\n",
       "      <th>agency_name</th>\n",
       "      <th>route_id</th>\n",
       "      <th>route_short_name</th>\n",
       "      <th>route_long_name</th>\n",
       "      <th>route_type</th>\n",
       "      <th>route_url</th>\n",
       "      <th>total_departures</th>\n",
       "      <th>median_headways</th>\n",
       "      <th>mean_headways</th>\n",
       "      <th>st_dev_headways</th>\n",
       "      <th>stop_count</th>\n",
       "      <th>service_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SF</td>\n",
       "      <td>San Francisco Municipal Transportation Agency</td>\n",
       "      <td>SF:1</td>\n",
       "      <td>1</td>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>3</td>\n",
       "      <td>https://SFMTA.com/1</td>\n",
       "      <td>10331</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>94</td>\n",
       "      <td>31 mins or more</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SF</td>\n",
       "      <td>San Francisco Municipal Transportation Agency</td>\n",
       "      <td>SF:10</td>\n",
       "      <td>10</td>\n",
       "      <td>TOWNSEND</td>\n",
       "      <td>3</td>\n",
       "      <td>https://SFMTA.com/10</td>\n",
       "      <td>1764</td>\n",
       "      <td>72.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>124</td>\n",
       "      <td>31 mins or more</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SF</td>\n",
       "      <td>San Francisco Municipal Transportation Agency</td>\n",
       "      <td>SF:12</td>\n",
       "      <td>12</td>\n",
       "      <td>FOLSOM-PACIFIC</td>\n",
       "      <td>3</td>\n",
       "      <td>https://SFMTA.com/12</td>\n",
       "      <td>921</td>\n",
       "      <td>70.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>85</td>\n",
       "      <td>31 mins or more</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SF</td>\n",
       "      <td>San Francisco Municipal Transportation Agency</td>\n",
       "      <td>SF:14</td>\n",
       "      <td>14</td>\n",
       "      <td>MISSION</td>\n",
       "      <td>3</td>\n",
       "      <td>https://SFMTA.com/14</td>\n",
       "      <td>2784</td>\n",
       "      <td>37.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>91</td>\n",
       "      <td>31 mins or more</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SF</td>\n",
       "      <td>San Francisco Municipal Transportation Agency</td>\n",
       "      <td>SF:14R</td>\n",
       "      <td>14R</td>\n",
       "      <td>MISSION RAPID</td>\n",
       "      <td>3</td>\n",
       "      <td>https://SFMTA.com/14R</td>\n",
       "      <td>844</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>44</td>\n",
       "      <td>31 mins or more</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  agency_id                                    agency_name route_id  \\\n",
       "0        SF  San Francisco Municipal Transportation Agency     SF:1   \n",
       "1        SF  San Francisco Municipal Transportation Agency    SF:10   \n",
       "2        SF  San Francisco Municipal Transportation Agency    SF:12   \n",
       "3        SF  San Francisco Municipal Transportation Agency    SF:14   \n",
       "4        SF  San Francisco Municipal Transportation Agency   SF:14R   \n",
       "\n",
       "  route_short_name route_long_name  route_type              route_url  \\\n",
       "0                1      CALIFORNIA           3    https://SFMTA.com/1   \n",
       "1               10        TOWNSEND           3   https://SFMTA.com/10   \n",
       "2               12  FOLSOM-PACIFIC           3   https://SFMTA.com/12   \n",
       "3               14         MISSION           3   https://SFMTA.com/14   \n",
       "4              14R   MISSION RAPID           3  https://SFMTA.com/14R   \n",
       "\n",
       "   total_departures  median_headways  mean_headways  st_dev_headways  \\\n",
       "0             10331             27.0           41.0             45.0   \n",
       "1              1764             72.0           83.0             84.0   \n",
       "2               921             70.0           84.0             65.0   \n",
       "3              2784             37.0           51.0             49.0   \n",
       "4               844             41.0           40.0             27.0   \n",
       "\n",
       "   stop_count    service_class  \n",
       "0          94  31 mins or more  \n",
       "1         124  31 mins or more  \n",
       "2          85  31 mins or more  \n",
       "3          91  31 mins or more  \n",
       "4          44  31 mins or more  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jan_all_day.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan_all_rt_hdwy_geo = gp.GeoDataFrame.merge(pretty_routes[['route_id','geometry']],\n",
    "                                            jan_all_day,\n",
    "                                            on='route_id',\n",
    "                                            how='inner',\n",
    "                                           indicator=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jan_all_rt_hdwy_geo.plot(figsize=(10,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan_am_rt_hdwy_geo = gp.GeoDataFrame.merge(pretty_routes[['route_id','geometry']],\n",
    "                                            jan_am_peak,\n",
    "                                            on='route_id',\n",
    "                                            how='inner',\n",
    "                                           indicator=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jan_am_rt_hdwy_geo.plot(figsize=(10,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_csv_geo(csv,gdf):\n",
    "    return gp.GeoDataFrame.merge(gdf[['route_id','geometry']],\n",
    "                                            csv,\n",
    "                                            on='route_id',\n",
    "                                            how='inner')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gdf = merge_csv_geo(jan_pm_peak,pretty_routes)"
   ]
  },
  {
   "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>route_id</th>\n",
       "      <th>geometry</th>\n",
       "      <th>agency_id</th>\n",
       "      <th>agency_name</th>\n",
       "      <th>route_short_name</th>\n",
       "      <th>route_long_name</th>\n",
       "      <th>route_type</th>\n",
       "      <th>route_url</th>\n",
       "      <th>total_departures</th>\n",
       "      <th>median_headways</th>\n",
       "      <th>mean_headways</th>\n",
       "      <th>st_dev_headways</th>\n",
       "      <th>stop_count</th>\n",
       "      <th>service_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3D:200</td>\n",
       "      <td>MULTILINESTRING ((-121.94474 38.01839, -121.94...</td>\n",
       "      <td>3D</td>\n",
       "      <td>Tri Delta Transit</td>\n",
       "      <td>200</td>\n",
       "      <td>Martinez / Pittsburg</td>\n",
       "      <td>3</td>\n",
       "      <td>http://12.155.17.20/RTT/Public/Schedule.aspx?R...</td>\n",
       "      <td>48</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17</td>\n",
       "      <td>31 mins or more</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3D:200</td>\n",
       "      <td>MULTILINESTRING ((-122.13925 38.01856, -122.13...</td>\n",
       "      <td>3D</td>\n",
       "      <td>Tri Delta Transit</td>\n",
       "      <td>200</td>\n",
       "      <td>Martinez / Pittsburg</td>\n",
       "      <td>3</td>\n",
       "      <td>http://12.155.17.20/RTT/Public/Schedule.aspx?R...</td>\n",
       "      <td>48</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17</td>\n",
       "      <td>31 mins or more</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3D:390</td>\n",
       "      <td>MULTILINESTRING ((-121.78095 37.99606, -121.78...</td>\n",
       "      <td>3D</td>\n",
       "      <td>Tri Delta Transit</td>\n",
       "      <td>390</td>\n",
       "      <td>Antioch BART / Pittsburg Bay Point BART</td>\n",
       "      <td>3</td>\n",
       "      <td>http://12.155.17.20/RTT/Public/Schedule.aspx?R...</td>\n",
       "      <td>241</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45</td>\n",
       "      <td>21-30 mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3D:390</td>\n",
       "      <td>MULTILINESTRING ((-121.94514 38.01852, -121.94...</td>\n",
       "      <td>3D</td>\n",
       "      <td>Tri Delta Transit</td>\n",
       "      <td>390</td>\n",
       "      <td>Antioch BART / Pittsburg Bay Point BART</td>\n",
       "      <td>3</td>\n",
       "      <td>http://12.155.17.20/RTT/Public/Schedule.aspx?R...</td>\n",
       "      <td>241</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45</td>\n",
       "      <td>21-30 mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3D:381</td>\n",
       "      <td>MULTILINESTRING ((-121.88527 38.03844, -121.88...</td>\n",
       "      <td>3D</td>\n",
       "      <td>Tri Delta Transit</td>\n",
       "      <td>381</td>\n",
       "      <td>Los Medanos College / Pittsburg Marina</td>\n",
       "      <td>3</td>\n",
       "      <td>http://12.155.17.20/RTT/Public/Schedule.aspx?R...</td>\n",
       "      <td>1001</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37</td>\n",
       "      <td>11-20 mins</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  route_id                                           geometry agency_id  \\\n",
       "0   3D:200  MULTILINESTRING ((-121.94474 38.01839, -121.94...        3D   \n",
       "1   3D:200  MULTILINESTRING ((-122.13925 38.01856, -122.13...        3D   \n",
       "2   3D:390  MULTILINESTRING ((-121.78095 37.99606, -121.78...        3D   \n",
       "3   3D:390  MULTILINESTRING ((-121.94514 38.01852, -121.94...        3D   \n",
       "4   3D:381  MULTILINESTRING ((-121.88527 38.03844, -121.88...        3D   \n",
       "\n",
       "         agency_name route_short_name  \\\n",
       "0  Tri Delta Transit              200   \n",
       "1  Tri Delta Transit              200   \n",
       "2  Tri Delta Transit              390   \n",
       "3  Tri Delta Transit              390   \n",
       "4  Tri Delta Transit              381   \n",
       "\n",
       "                           route_long_name  route_type  \\\n",
       "0                     Martinez / Pittsburg           3   \n",
       "1                     Martinez / Pittsburg           3   \n",
       "2  Antioch BART / Pittsburg Bay Point BART           3   \n",
       "3  Antioch BART / Pittsburg Bay Point BART           3   \n",
       "4   Los Medanos College / Pittsburg Marina           3   \n",
       "\n",
       "                                           route_url  total_departures  \\\n",
       "0  http://12.155.17.20/RTT/Public/Schedule.aspx?R...                48   \n",
       "1  http://12.155.17.20/RTT/Public/Schedule.aspx?R...                48   \n",
       "2  http://12.155.17.20/RTT/Public/Schedule.aspx?R...               241   \n",
       "3  http://12.155.17.20/RTT/Public/Schedule.aspx?R...               241   \n",
       "4  http://12.155.17.20/RTT/Public/Schedule.aspx?R...              1001   \n",
       "\n",
       "   median_headways  mean_headways  st_dev_headways  stop_count  \\\n",
       "0             63.0           63.0              1.0          17   \n",
       "1             63.0           63.0              1.0          17   \n",
       "2             30.0           30.0              8.0          45   \n",
       "3             30.0           30.0              8.0          45   \n",
       "4             15.0           17.0              6.0          37   \n",
       "\n",
       "     service_class  \n",
       "0  31 mins or more  \n",
       "1  31 mins or more  \n",
       "2       21-30 mins  \n",
       "3       21-30 mins  \n",
       "4       11-20 mins  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_gdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join routes with headway csvs for the following transit service time periods July 2020:\n",
    "\n",
    "- AM Peak (6:00-10:00AM)\n",
    "- Midday (10:00AM-3:00PM)\n",
    "- PM Peak (3:00-7:00PM)\n",
    "- Latenight (7:00PM-6:00AM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "june_all_day = pd.read_csv(os.path.join(box_data_dir,'2020-06/all_6am_6am_blue_ribbon_calcs.csv'))\n",
    "june_am_peak = pd.read_csv(os.path.join(box_data_dir,'2020-06/am_blue_ribbon_calcs.csv'))\n",
    "june_mid_day = pd.read_csv(os.path.join(box_data_dir,'2020-06/mid_blue_ribbon_calcs.csv'))\n",
    "june_pm_peak = pd.read_csv(os.path.join(box_data_dir,'2020-06/pm_blue_ribbon_calcs.csv'))\n",
    "june_late_day = pd.read_csv(os.path.join(box_data_dir,'2020-06/late_blue_ribbon_calcs.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 225 entries, 0 to 224\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   agency_id         225 non-null    object \n",
      " 1   agency_name       225 non-null    object \n",
      " 2   route_id          225 non-null    object \n",
      " 3   route_short_name  225 non-null    object \n",
      " 4   route_long_name   225 non-null    object \n",
      " 5   route_type        225 non-null    int64  \n",
      " 6   route_url         225 non-null    object \n",
      " 7   total_departures  225 non-null    int64  \n",
      " 8   median_headways   225 non-null    float64\n",
      " 9   mean_headways     225 non-null    float64\n",
      " 10  st_dev_headways   216 non-null    float64\n",
      " 11  stop_count        225 non-null    int64  \n",
      " 12  service_class     225 non-null    object \n",
      "dtypes: float64(3), int64(3), object(7)\n",
      "memory usage: 23.0+ KB\n"
     ]
    }
   ],
   "source": [
    "june_am_peak.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "june_all_rt_hdwy_geo = gp.GeoDataFrame.merge(pretty_routes[['route_id','geometry']],\n",
    "                                            june_all_day,\n",
    "                                            on='route_id',\n",
    "                                            how='inner',\n",
    "                                           indicator=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "june_all_rt_hdwy_geo.plot(figsize=(10,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}