This notebook associates the distance and frequency of the most frequent rail and bus stops to a parcel dataset given the output from Count High Frequency Routes at Stops in the Esri BBB Toolbox.

Associate_Frequent_Transit_to_Parcel_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Associate Frequent Transit to Parcel Data\n",
    "This notebook associates the distance and frequency of the most frequent rail and bus stops to a parcel dataset given the output from \"Count High Frequency Routes at Stops \" in the [Esri's BBB Toolbox](https://esri.github.io/public-transit-tools/BetterBusBuffers.html).\n",
    "\n",
    "Author: David Wasserman\n",
    "\n",
    "License: Apache 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arcpy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gdb = r\"D:\\Documents\\Github\\sample-data-compendium\\data\\sample-data-compendium.gdb\\Sample_Data\"\n",
    "parcels = os.path.join(data_gdb,\"Parcel_Data_SF\")\n",
    "out_parcels = os.path.join(data_gdb,\"Parcel_Data_WTransit_Per_Mi_Proximity\")\n",
    "bus_freq_stops = os.path.join(data_gdb,\"SFMTA_Stops_15_Min_Mon_AM_Peak\")\n",
    "rail_freq_stops = os.path.join(data_gdb,\"SF_Rail_Stops_15_Min_Mon_Am_Peak\")\n",
    "temp_ws = r\"in_memory\"\n",
    "arcpy.env.overwriteOutput = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arcgis_table_to_df(in_fc, input_fields, query=\"\"):\n",
    "    \"\"\"Function will convert an arcgis table into a pandas dataframe with an object ID index, and the selected\n",
    "    input fields using an arcpy.da.SearchCursor.\"\"\"\n",
    "    OIDFieldName = arcpy.Describe(in_fc).OIDFieldName\n",
    "    final_fields = [OIDFieldName] + input_fields\n",
    "    data = [row for row in arcpy.da.SearchCursor(in_fc,final_fields,where_clause=query)]\n",
    "    fc_dataframe = pd.DataFrame(data,columns=final_fields)\n",
    "    fc_dataframe = fc_dataframe.set_index(OIDFieldName,drop=True)\n",
    "    return fc_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop_id</th>\n",
       "      <th>stop_name</th>\n",
       "      <th>NumTripsPerHr</th>\n",
       "      <th>rte_count</th>\n",
       "      <th>MetHdWyLim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBJECTID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sfmtagtfs:390</td>\n",
       "      <td>19th Avenue &amp; Holloway St</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sfmtagtfs:913</td>\n",
       "      <td>DUBLIN ST &amp; LAGRANDE AVE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sfmtagtfs:3003</td>\n",
       "      <td>2nd St &amp; Brannan St</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sfmtagtfs:3004</td>\n",
       "      <td>2nd St &amp; Brannan St</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sfmtagtfs:3009</td>\n",
       "      <td>2nd St &amp; Harrison St</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 stop_id                  stop_name  NumTripsPerHr  rte_count  \\\n",
       "OBJECTID                                                                        \n",
       "1          sfmtagtfs:390  19th Avenue & Holloway St      17.333333        3.0   \n",
       "2          sfmtagtfs:913   DUBLIN ST & LAGRANDE AVE       5.000000        2.0   \n",
       "3         sfmtagtfs:3003        2nd St & Brannan St       4.666667        1.0   \n",
       "4         sfmtagtfs:3004        2nd St & Brannan St       3.333333        1.0   \n",
       "5         sfmtagtfs:3009       2nd St & Harrison St       4.666667        1.0   \n",
       "\n",
       "          MetHdWyLim  \n",
       "OBJECTID              \n",
       "1                3.0  \n",
       "2                0.0  \n",
       "3                1.0  \n",
       "4                1.0  \n",
       "5                1.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency_fields = ['stop_id', 'stop_name',  'NumTripsPerHr', 'rte_count', 'MetHdWyLim']\n",
    "stops_df = arcgis_table_to_df(bus_freq_stops,frequency_fields)\n",
    "stops_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop_id</th>\n",
       "      <th>stop_name</th>\n",
       "      <th>NumTripsPerHr</th>\n",
       "      <th>rte_count</th>\n",
       "      <th>MetHdWyLim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBJECTID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>caltraingtfs:70011</td>\n",
       "      <td>San Francisco Caltrain</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>caltraingtfs:70012</td>\n",
       "      <td>San Francisco Caltrain</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>caltraingtfs:70021</td>\n",
       "      <td>22nd Street Caltrain</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>caltraingtfs:70022</td>\n",
       "      <td>22nd Street Caltrain</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>caltraingtfs:70031</td>\n",
       "      <td>Bayshore Caltrain</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     stop_id               stop_name  NumTripsPerHr  \\\n",
       "OBJECTID                                                              \n",
       "1         caltraingtfs:70011  San Francisco Caltrain       4.000000   \n",
       "2         caltraingtfs:70012  San Francisco Caltrain       5.000000   \n",
       "3         caltraingtfs:70021    22nd Street Caltrain       1.000000   \n",
       "4         caltraingtfs:70022    22nd Street Caltrain       4.666667   \n",
       "5         caltraingtfs:70031       Bayshore Caltrain       1.000000   \n",
       "\n",
       "          rte_count  MetHdWyLim  \n",
       "OBJECTID                         \n",
       "1               3.0         0.0  \n",
       "2               3.0         0.0  \n",
       "3               2.0         0.0  \n",
       "4               2.0         0.0  \n",
       "5               2.0         0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rail_df = arcgis_table_to_df(rail_freq_stops,frequency_fields)\n",
    "rail_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IN_FID</th>\n",
       "      <th>NEAR_FID</th>\n",
       "      <th>NEAR_DIST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBJECTID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1877</td>\n",
       "      <td>83</td>\n",
       "      <td>5236.572430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1878</td>\n",
       "      <td>83</td>\n",
       "      <td>5154.963688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2345</td>\n",
       "      <td>83</td>\n",
       "      <td>5147.575453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2346</td>\n",
       "      <td>83</td>\n",
       "      <td>5277.429426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2348</td>\n",
       "      <td>83</td>\n",
       "      <td>5161.220061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          IN_FID  NEAR_FID    NEAR_DIST\n",
       "OBJECTID                               \n",
       "1           1877        83  5236.572430\n",
       "2           1878        83  5154.963688\n",
       "3           2345        83  5147.575453\n",
       "4           2346        83  5277.429426\n",
       "5           2348        83  5161.220061"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_rail_near = os.path.join(temp_ws,\"rail_near_parcels\")\n",
    "arcpy.GenerateNearTable_analysis(parcels,rail_freq_stops,temp_rail_near,search_radius=\"1 Mile\",closest=False)\n",
    "ra_tab = arcgis_table_to_df(temp_rail_near,['IN_FID', 'NEAR_FID', 'NEAR_DIST'])\n",
    "ra_tab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IN_FID</th>\n",
       "      <th>NEAR_FID</th>\n",
       "      <th>NEAR_DIST</th>\n",
       "      <th>NumTripsPerHr</th>\n",
       "      <th>rte_count</th>\n",
       "      <th>MetHdWyLim</th>\n",
       "      <th>Frequent_Rte_Per_Mi_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>113531.000000</td>\n",
       "      <td>113531.000000</td>\n",
       "      <td>113531.000000</td>\n",
       "      <td>113531.000000</td>\n",
       "      <td>113531.000000</td>\n",
       "      <td>113531.000000</td>\n",
       "      <td>113531.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>131070.557354</td>\n",
       "      <td>71.201487</td>\n",
       "      <td>3015.905242</td>\n",
       "      <td>30.894091</td>\n",
       "      <td>7.364790</td>\n",
       "      <td>7.126512</td>\n",
       "      <td>18.534845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>65505.059879</td>\n",
       "      <td>25.875688</td>\n",
       "      <td>1288.259238</td>\n",
       "      <td>10.065514</td>\n",
       "      <td>1.819164</td>\n",
       "      <td>2.494991</td>\n",
       "      <td>219.868855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1877.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>113435.500000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>2004.152365</td>\n",
       "      <td>33.666667</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.365951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>143320.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>2983.715362</td>\n",
       "      <td>33.666667</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.831235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>184541.500000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>4103.734972</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>19.386197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>217790.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>5279.906758</td>\n",
       "      <td>37.666667</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>42240.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              IN_FID       NEAR_FID      NEAR_DIST  NumTripsPerHr  \\\n",
       "count  113531.000000  113531.000000  113531.000000  113531.000000   \n",
       "mean   131070.557354      71.201487    3015.905242      30.894091   \n",
       "std     65505.059879      25.875688    1288.259238      10.065514   \n",
       "min      1877.000000       1.000000       0.000000       1.000000   \n",
       "25%    113435.500000      68.000000    2004.152365      33.666667   \n",
       "50%    143320.000000      74.000000    2983.715362      33.666667   \n",
       "75%    184541.500000      86.000000    4103.734972      34.000000   \n",
       "max    217790.000000     100.000000    5279.906758      37.666667   \n",
       "\n",
       "           rte_count     MetHdWyLim  Frequent_Rte_Per_Mi_Ratio  \n",
       "count  113531.000000  113531.000000              113531.000000  \n",
       "mean        7.364790       7.126512                  18.534845  \n",
       "std         1.819164       2.494991                 219.868855  \n",
       "min         1.000000       0.000000                   0.000000  \n",
       "25%         8.000000       8.000000                   9.365951  \n",
       "50%         8.000000       8.000000                  12.831235  \n",
       "75%         8.000000       8.000000                  19.386197  \n",
       "max         8.000000       8.000000               42240.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "near_rail_df = pd.merge(ra_tab,rail_df,left_on=\"NEAR_FID\",right_index=True)\n",
    "near_rail_df[\"Frequent_Rte_Per_Mi_Ratio\"] = near_rail_df[\"MetHdWyLim\"]/((near_rail_df[\"NEAR_DIST\"]+1)/5280)\n",
    "near_rail_df.Frequent_Rte_Per_Mi_Ratio.describe()\n",
    "groups = near_rail_df.groupby([\"IN_FID\"])[\"Frequent_Rte_Per_Mi_Ratio\"].idxmax() # Get index of max headlim count\n",
    "rail_max_arg = near_rail_df.loc[groups] # use it to filter out the df by labels\n",
    "rail_max_arg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>IN_FID</th>\n",
       "      <th>NEAR_FID</th>\n",
       "      <th>NEAR_DIST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBJECTID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>95.954099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1691</td>\n",
       "      <td>338.092971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1925</td>\n",
       "      <td>356.115646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1690</td>\n",
       "      <td>447.806719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1926</td>\n",
       "      <td>458.802706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          IN_FID  NEAR_FID   NEAR_DIST\n",
       "OBJECTID                              \n",
       "1              1        72   95.954099\n",
       "2              1      1691  338.092971\n",
       "3              1      1925  356.115646\n",
       "4              1      1690  447.806719\n",
       "5              1      1926  458.802706"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_bus_near = os.path.join(temp_ws,\"bus_near_parcels\")\n",
    "arcpy.GenerateNearTable_analysis(parcels,bus_freq_stops,temp_bus_near,search_radius=\"0.5 Mile\",closest=False)\n",
    "bus_tab = arcgis_table_to_df(temp_bus_near,['IN_FID', 'NEAR_FID', 'NEAR_DIST'])\n",
    "bus_tab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>IN_FID</th>\n",
       "      <th>NEAR_FID</th>\n",
       "      <th>NEAR_DIST</th>\n",
       "      <th>NumTripsPerHr</th>\n",
       "      <th>rte_count</th>\n",
       "      <th>MetHdWyLim</th>\n",
       "      <th>Frequent_Rte_Per_Mi_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>217181.000000</td>\n",
       "      <td>217181.000000</td>\n",
       "      <td>217181.000000</td>\n",
       "      <td>217181.000000</td>\n",
       "      <td>217181.000000</td>\n",
       "      <td>217181.000000</td>\n",
       "      <td>217181.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>108757.122156</td>\n",
       "      <td>1791.991496</td>\n",
       "      <td>568.188116</td>\n",
       "      <td>12.725105</td>\n",
       "      <td>2.281806</td>\n",
       "      <td>2.129993</td>\n",
       "      <td>75.437514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>62904.353704</td>\n",
       "      <td>1006.091794</td>\n",
       "      <td>562.428278</td>\n",
       "      <td>7.744791</td>\n",
       "      <td>1.373542</td>\n",
       "      <td>1.257416</td>\n",
       "      <td>335.290993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54296.000000</td>\n",
       "      <td>904.000000</td>\n",
       "      <td>183.419581</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.217874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>108591.000000</td>\n",
       "      <td>1983.000000</td>\n",
       "      <td>375.801240</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.754289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>163495.000000</td>\n",
       "      <td>2628.000000</td>\n",
       "      <td>748.533246</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>49.160420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>217790.000000</td>\n",
       "      <td>3530.000000</td>\n",
       "      <td>2640.000985</td>\n",
       "      <td>43.333333</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>26400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              IN_FID       NEAR_FID      NEAR_DIST  NumTripsPerHr  \\\n",
       "count  217181.000000  217181.000000  217181.000000  217181.000000   \n",
       "mean   108757.122156    1791.991496     568.188116      12.725105   \n",
       "std     62904.353704    1006.091794     562.428278       7.744791   \n",
       "min         1.000000       1.000000       0.000000       1.333333   \n",
       "25%     54296.000000     904.000000     183.419581       6.666667   \n",
       "50%    108591.000000    1983.000000     375.801240      11.000000   \n",
       "75%    163495.000000    2628.000000     748.533246      17.333333   \n",
       "max    217790.000000    3530.000000    2640.000985      43.333333   \n",
       "\n",
       "           rte_count     MetHdWyLim  Frequent_Rte_Per_Mi_Ratio  \n",
       "count  217181.000000  217181.000000              217181.000000  \n",
       "mean        2.281806       2.129993                  75.437514  \n",
       "std         1.373542       1.257416                 335.290993  \n",
       "min         1.000000       0.000000                   0.000000  \n",
       "25%         1.000000       1.000000                  14.217874  \n",
       "50%         2.000000       2.000000                  24.754289  \n",
       "75%         3.000000       3.000000                  49.160420  \n",
       "max         8.000000       7.000000               26400.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "near_bus_df = pd.merge(bus_tab,stops_df,left_on=\"NEAR_FID\",right_index=True)\n",
    "near_bus_df[\"Frequent_Rte_Per_Mi_Ratio\"] = near_bus_df[\"MetHdWyLim\"]/((near_bus_df[\"NEAR_DIST\"]+1)/5280)\n",
    "groups = near_bus_df.groupby([\"IN_FID\"])[\"Frequent_Rte_Per_Mi_Ratio\"].idxmax() # Get index of max headlim count\n",
    "bus_max_arg = near_bus_df.loc[groups] # use it to filter out the df by labels\n",
    "bus_max_arg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Rail_IN_FID</th>\n",
       "      <th>Rail_NEAR_FID</th>\n",
       "      <th>Rail_NEAR_DIST</th>\n",
       "      <th>Rail_stop_id</th>\n",
       "      <th>Rail_stop_name</th>\n",
       "      <th>Rail_NumTripsPerHr</th>\n",
       "      <th>Rail_rte_count</th>\n",
       "      <th>Rail_MetHdWyLim</th>\n",
       "      <th>Rail_Frequent_Rte_Per_Mi_Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBJECTID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1877</td>\n",
       "      <td>83</td>\n",
       "      <td>5236.572430</td>\n",
       "      <td>bart2018gtfs:EMBR</td>\n",
       "      <td>Embarcadero BART Station</td>\n",
       "      <td>37.333333</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.064805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1878</td>\n",
       "      <td>83</td>\n",
       "      <td>5154.963688</td>\n",
       "      <td>bart2018gtfs:EMBR</td>\n",
       "      <td>Embarcadero BART Station</td>\n",
       "      <td>37.333333</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.192455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2345</td>\n",
       "      <td>83</td>\n",
       "      <td>5147.575453</td>\n",
       "      <td>bart2018gtfs:EMBR</td>\n",
       "      <td>Embarcadero BART Station</td>\n",
       "      <td>37.333333</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.204211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2346</td>\n",
       "      <td>83</td>\n",
       "      <td>5277.429426</td>\n",
       "      <td>bart2018gtfs:EMBR</td>\n",
       "      <td>Embarcadero BART Station</td>\n",
       "      <td>37.333333</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.002380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2348</td>\n",
       "      <td>83</td>\n",
       "      <td>5161.220061</td>\n",
       "      <td>bart2018gtfs:EMBR</td>\n",
       "      <td>Embarcadero BART Station</td>\n",
       "      <td>37.333333</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.182526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Rail_IN_FID  Rail_NEAR_FID  Rail_NEAR_DIST       Rail_stop_id  \\\n",
       "OBJECTID                                                                  \n",
       "1                1877             83     5236.572430  bart2018gtfs:EMBR   \n",
       "2                1878             83     5154.963688  bart2018gtfs:EMBR   \n",
       "3                2345             83     5147.575453  bart2018gtfs:EMBR   \n",
       "4                2346             83     5277.429426  bart2018gtfs:EMBR   \n",
       "5                2348             83     5161.220061  bart2018gtfs:EMBR   \n",
       "\n",
       "                    Rail_stop_name  Rail_NumTripsPerHr  Rail_rte_count  \\\n",
       "OBJECTID                                                                 \n",
       "1         Embarcadero BART Station           37.333333             8.0   \n",
       "2         Embarcadero BART Station           37.333333             8.0   \n",
       "3         Embarcadero BART Station           37.333333             8.0   \n",
       "4         Embarcadero BART Station           37.333333             8.0   \n",
       "5         Embarcadero BART Station           37.333333             8.0   \n",
       "\n",
       "          Rail_MetHdWyLim  Rail_Frequent_Rte_Per_Mi_Ratio  \n",
       "OBJECTID                                                   \n",
       "1                     8.0                        8.064805  \n",
       "2                     8.0                        8.192455  \n",
       "3                     8.0                        8.204211  \n",
       "4                     8.0                        8.002380  \n",
       "5                     8.0                        8.182526  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rail_max_arg.columns = [\"Rail_\"+i for i in rail_max_arg]\n",
    "rail_max_arg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Bus_IN_FID</th>\n",
       "      <th>Bus_NEAR_FID</th>\n",
       "      <th>Bus_NEAR_DIST</th>\n",
       "      <th>Bus_stop_id</th>\n",
       "      <th>Bus_stop_name</th>\n",
       "      <th>Bus_NumTripsPerHr</th>\n",
       "      <th>Bus_rte_count</th>\n",
       "      <th>Bus_MetHdWyLim</th>\n",
       "      <th>Bus_Frequent_Rte_Per_Mi_Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBJECTID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>95.954099</td>\n",
       "      <td>sfmtagtfs:3092</td>\n",
       "      <td>Beach St &amp; Mason St</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.458760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2</td>\n",
       "      <td>1691</td>\n",
       "      <td>141.314885</td>\n",
       "      <td>sfmtagtfs:5175</td>\n",
       "      <td>Jefferson St &amp; Taylor St</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.100827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>3</td>\n",
       "      <td>3183</td>\n",
       "      <td>92.000129</td>\n",
       "      <td>sfmtagtfs:7038</td>\n",
       "      <td>Powell St &amp; Beach St</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113.548230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>4</td>\n",
       "      <td>3183</td>\n",
       "      <td>72.301001</td>\n",
       "      <td>sfmtagtfs:7038</td>\n",
       "      <td>Powell St &amp; Beach St</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>144.063517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>5</td>\n",
       "      <td>1192</td>\n",
       "      <td>64.884856</td>\n",
       "      <td>sfmtagtfs:4530</td>\n",
       "      <td>The Embarcadero &amp; Stockton St</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.139812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Bus_IN_FID  Bus_NEAR_FID  Bus_NEAR_DIST     Bus_stop_id  \\\n",
       "OBJECTID                                                            \n",
       "1                  1            72      95.954099  sfmtagtfs:3092   \n",
       "70                 2          1691     141.314885  sfmtagtfs:5175   \n",
       "142                3          3183      92.000129  sfmtagtfs:7038   \n",
       "215                4          3183      72.301001  sfmtagtfs:7038   \n",
       "284                5          1192      64.884856  sfmtagtfs:4530   \n",
       "\n",
       "                          Bus_stop_name  Bus_NumTripsPerHr  Bus_rte_count  \\\n",
       "OBJECTID                                                                    \n",
       "1                   Beach St & Mason St           6.333333            1.0   \n",
       "70             Jefferson St & Taylor St           6.666667            1.0   \n",
       "142                Powell St & Beach St          12.333333            2.0   \n",
       "215                Powell St & Beach St          12.333333            2.0   \n",
       "284       The Embarcadero & Stockton St           7.000000            1.0   \n",
       "\n",
       "          Bus_MetHdWyLim  Bus_Frequent_Rte_Per_Mi_Ratio  \n",
       "OBJECTID                                                 \n",
       "1                    1.0                      54.458760  \n",
       "70                   1.0                      37.100827  \n",
       "142                  2.0                     113.548230  \n",
       "215                  2.0                     144.063517  \n",
       "284                  1.0                      80.139812  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bus_max_arg.columns = [\"Bus_\"+i for i in bus_max_arg]\n",
    "bus_max_arg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parcels copied.\n"
     ]
    }
   ],
   "source": [
    "arcpy.CopyFeatures_management(parcels,out_parcels)\n",
    "print(\"Parcels copied.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OBJECTID'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OIDFieldName = arcpy.Describe(out_parcels).OIDFieldName\n",
    "OIDFieldName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rail records joined\n"
     ]
    }
   ],
   "source": [
    "rail_records= rail_max_arg.select_dtypes(include=[np.number]).to_records() \n",
    "arcpy.da.ExtendTable(out_parcels,OIDFieldName,rail_records,\"Rail_IN_FID\",append_only=False)\n",
    "print(\"Rail records joined\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bus records joined\n"
     ]
    }
   ],
   "source": [
    "bus_records= bus_max_arg.select_dtypes(include=[np.number]).to_records() \n",
    "arcpy.da.ExtendTable(out_parcels,OIDFieldName,bus_records,\"Bus_IN_FID\",append_only=False)\n",
    "print(\"Bus records joined\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001CD7638EB00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD753A6F28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD753D45F8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001CD63D6AC88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD7619B358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD7619B390>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001CE57A930B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD754BE748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD754E5DD8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bus_max_arg.hist(figsize = (10,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001CD6465BE10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD64985940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD644F6F60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001CD64525630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD64550CC0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD64550CF8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001CD645A8A20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD645DB0F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001CD64603780>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rail_max_arg.hist(figsize = (10,7))"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}