1.6 - MuniBoard Survey.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import pathlib\n",
    "from pathlib import Path\n",
    "import datetime\n",
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import itertools\n",
    "# set the working directory\n",
    "BASE_DIR = Path.cwd()\n",
    "\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "'\\nLooking for a few basic summaries of:\\n-\\tFrequency of 0, 1, 2 and 3+ transfers.\\n-\\tFrequency of walk access distance to transit, in 200m bins\\n-\\tFrequency of walk egress distance from transit, in 200m bins\\n-\\tDistribution of unweighted travel times\\n'"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Looking for a few basic summaries of:\n",
    "-\tFrequency of 0, 1, 2 and 3+ transfers.\n",
    "-\tFrequency of walk access distance to transit, in 200m bins\n",
    "-\tFrequency of walk egress distance from transit, in 200m bins\n",
    "-\tDistribution of unweighted travel times\n",
    "\"\"\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "dfboardings = pd.read_csv(BASE_DIR.parent.joinpath(\"Muni on-board\",\"Muni 2017 TPS Access and Egress Distances.csv\"),low_memory=False)\n",
    "relevant_cols = ['ID', 'operator', 'access_mode', 'dest_purp', 'egress_mode',\n",
    "                 'orig_purp', 'workers', 'tour_purp', 'tour_purp_case', 'worker_numeric_cat', 'auto_suff',\n",
    "                 'first_before_operator_detail', 'second_before_operator_detail', 'third_before_operator_detail',\n",
    "                 'first_after_operator_detail', 'second_after_operator_detail', 'third_after_operator_detail',\n",
    "                 'first_before_operator', 'second_before_operator', 'third_before_operator',\n",
    "                 'first_after_operator', 'second_after_operator', 'third_after_operator',\n",
    "                 'first_before_technology', 'second_before_technology', 'third_before_technology',\n",
    "                 'first_after_technology', 'second_after_technology', 'third_after_technology',\n",
    "                 'transfer_from', 'transfer_to', 'first_board_tech', 'last_alight_tech',\n",
    "                 'boardings', 'day_part', 'trip_weight', 'survey_time',\n",
    "                 'agg_tour_purp',\n",
    "                 'access_mode_model', 'egress_mode_model', 'auto_suff_model',\n",
    "                 'exp_factor', 'boardWeight_2015', 'tripWeight_2015', 'nTransfers', 'period', 'BEST_MODE', 'TRANSFER_TYPE', 'final_tripWeight_2015', 'final_boardWeight_2015', 'final_expansionFactor', 'orig_dest_dist', 'orig_firstboard_dist', 'orig_surveyboard_dist', 'survey_alight_dest_dist', 'last_alight_dest_dist']\n",
    "dfselected = dfboardings[relevant_cols]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 841.68x595.44 with 1 Axes>",
      "image/png": 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JUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGXWWQqqprVtVRVXWttSgIAABg0V1lkOruK5K8pLsvXYN6AAAAFt68Q/veUVX3X9VKAAAANoi95tzv2kneXFUfSXJ2kt66obsfsRqFAQAALKp5g9SZ0wcAAGCPN1eQ6u7nrnYhAAAAG8Xc059X1T2r6lVV9Y5pfXNV3X31SgMAAFhMcwWpqnpCkpcn+XySO0/NlyT501WqCwAAYGHN2yP15CRHdvefJblyavtMklusRlEAAACLbN4gtV9ms/UlP5qxb+8kl+32igAAABbcvEHqg0mevqztiUnev3vLAQAAWHzzTn/+hMxeynt0kv2q6rNJvpfkfqtWGQAAwILabpCqqsO6+xNJ0t3frKrDkxye5ODMhvmd2t1Xbu94AACAq6sd9UidlGT/JKmqz3f3zyc5dfoAAADssXYUpC6oqvsl+VSSn66qmyap5Tt195dWqzgAAIBFtKMg9aQkf5nZUL5rJPniCvt0kmvu/rIAAAAW13Zn7evut3b3zbp77yQ/6O5rrPARogAAgD3OvNOfX3/pSlVdp6qutQr1AAAALLx5g9QLquq2SVJV901yfpLvVtX9V60yAACABTVvkHpokjOn5WcleViSByR5wWoUBQAAsMjmfSHvT3T3D6rq+kl+trvfkiRVdfDqlQYAALCY5g1Sn6uqhya5WZL3JklVHZjkktUqDAAAYFHNG6Qel+TYJJcleczUdu8k71mNogAAABbZXEGqu09Lcsdlba9P8vrVKAoAAGCRzdsjlaq6RZLDkuy7tL27j5vj2NsleWmSK5Oc1t1PqaqnJfmNJGcleVR3X74rbfPeBwAAwK6aa9a+qvrjJJ9I8odJHr7k87A5r3NWkrt39xFJblBVd0lyt2n9k0keWFU32Nm2OWsAAADYLebtkXpyktt29yd35iLdfc6S1cuT/FKSE6f1EzKbXv37u9D2D0uvV1XHJDkmSQ466KCdKRkAAGC75n2P1CVJPrOrF6uqX06yKckFSS6ami9McsD02dm2bXT3K7p7c3dv3rRp066WDQAAsI15g9Qzk/x/VfXTVXWNpZ95L1RV10vyssxm/bswyf7Tpv0zC1a70gYAALBm5g1Cr0lydJKvZTY07/IkP5x+XqWq2ivJ65I8dRrmd1qSu0ybj0xyyi62AQAArJl5n5G66S5e58FJDk/yoqpKkmck+WBVnZzkq0n+srsvq6qdatvF2gAAAIZUd693Datq8+bNffrpp++28z3s2HfttnPBPF73pPuudwkAAHukqvpYd29eadvIe6QekNmQugOT1Nb27n7ELlcIAACwgcz7HqlnJ/mbaf8HJ/lOknvHRA8AAMAeaN7JJo5Kcs/ufkqSy6af909yyGoVBgAAsKjmDVIHdPeZ0/JlVbV3d5+aH82eBwAAsMeY9xmpL1bVL3X3vyc5M8kfVNV3k3x39UoDAABYTPMGqf83yfWn5acneUOSfZM8bjWKAgAAWGRXGaSq6hpJtmR68e00pO9mq1wXAADAwrrKZ6S6+8okx3f3ZWtQDwAAwMKbd7KJD1bV7Ve1EgAAgA1i3mekzkry7qo6PsnZSXrrhu5+1moUBgAAsKjmDVLXSfK2afkmq1MKAADAxjBXkOruR692IQAAABvFXM9IVdX522n/1u4tBwAAYPHNO9nE3ssbqmrvJNfcveUAAAAsvh0O7auqkzKbWOLaVfXBZZtvkuTDq1UYAADAorqqZ6T+NkklOTzJq5a0d5Jzk7xvleoCAABYWDsMUt39v5Okqk7p7s+sTUkAAACLba5npIQoAACAH5l3sgkAAAAmghQAAMCg7QapqjplyfKz16YcAACAxbejHqmbV9W1p+U/XItiAAAANoIdzdp3fJLPVdVXklxnhfdIJUm6+86rURgAAMCi2m6Q6u5HV9URSQ7Jj79HCgAAYI91Ve+ROjnJyVW1z9Z3SgEAAOzpdhikturu46rqrkkekeTGSb6e5LXd/f7VKw0AAGAxzTX9eVX9XpK/T3JOkn9M8s0kb6yqo1exNgAAgIU0V49Ukv+W5J7d/YmtDVX1piRvSfLK1SgMAABgUc37Qt7rJ/nUsrbPJrne7i0HAABg8c0bpE5O8pKq+okkqarrJvnzJB9ercIAAAAW1bxB6rFJDktyYVWdm+SCaf33V6kuAACAhTXvrH3fTHLnqrpJkhsl+UZ3f21VKwMAAFhQ8042kSSZwpMABQAA7NHmHdoHAADARJACAAAYJEgBAAAMmusZqar6xSTf6e5zq2rfJE9LcmWSP+/uH6xmgQAAAItm3h6pNyY5YFp+cZI7J7l9kr9ZhZoAAAAW2ryz9h3S3Z+tqkryoCS/mOSSJF9etcoAAAAW1LxBaktV7ZdZgPpqd3+7qvZKcu3VKw0AAGAxzRuk3pDkfUn2S/Kyqe020SMFAADsgeYKUt39lKq6V5LLu/v9U/OVSZ6yapUBAAAsqHl7pNLd76mqn6mq23f3Kd19+moWBgAAsKjmmrWvqg6qqg8l+UySE6a236qqv13N4gAAABbRvNOf/02Sd2X2jNTlU9t7k9xzNYoCAABYZPMO7bttkvt295VV1UnS3RdW1U+uXmkAAACLad4eqXOT3GxpQ1X9YpKv7vaKAAAAFty8QerFSd5ZVY9OsldVPSTJm5K8cNUqAwAAWFDzTn9+XFV9J8nvJzk7ySOTPLO737aKtQEAACykkenPj09y/CrWAgAAsCFsN0hV1cO7+7XT8lHb26+7j1uNwgAAABbVjnqkHpLktdPyw7ezTycRpAAAgD3KdoNUd//6kuW7rU05AAAAi2+uWfuq6l5VdfNlbTevKi/kBQAA9jjzTn/+V0m+t6zt4qkdAABgjzJvkLpBd39zWds3k/zUbq4HAABg4c0bpL5UVXdf1nbXJF/eveUAAAAsvnnfI/WcJP9YVa9K8sUkP5fk0dMHAABgjzJXj9T0Mt57JblukvtOP+89tQMAAOxR5u2RSnefmuTUVawFAABgQ5grSFXVPkkeleTWSfZduq27H7HbqwIAAFhg8/ZI/e8khyV5R5JzV68cAACAxTdvkLpPkpt29wWrWAsAAMCGMO/0519Ncq3VLAQAAGCjmLdH6u+SHF9Vx2bZ0L7uft9urwoAAGCBzRukHj/9fMGy9k7ys7uvHAAAgMU3V5Dq7puudiEAAAAbxbzPSKWq9q6qO1XVb0/r162q665eaQAAAItpriBVVbdK8rkkr0zyqqn5LkmOW6W6AAAAFta8PVIvT/Ks7v6FJJdPbR9IcsQ8B1fVjarqjKraUlV7VdUhVXVuVZ1YVe9Zst/Tqurkqnp9Ve090gYAALBW5g1Sv5TkddNyJ0l3fz/JdeY8/vwk90hyypK293b3Xbv7XklSVTdIcrfuPiLJJ5M8cN62OWsAAADYLeYNUl9J8itLG6rqtkm+MM/B3b2lu7+7rPluVXVSVT1lWt+c5MRp+YQkdxhoAwAAWDPzTn/+zCTvqqq/TrJPVT0jyWOTHL2T1/1mkpsnuTSz91P9a5IDklw0bb9wWp+3bRtVdUySY5LkoIMO2skSAQAAVjZXj1R3vzPJfZJsyuzZqIOTPKi737PDA7d/vku7+/vd/cMk70xyaGahaP9pl/2TXDDQtvz8r+juzd29edOmTTtTIgAAwHbNPf15d3+8ux/X3fft7sd298d29qJVtd+S1V9N8sUkp2U2E2CSHJnZ81TztgEAAKyZuYb2VdXztretu581x/F7J3l3ksOS/EuSD1bVAzIb2ndSd3902u+DVXVykq8m+cvuvmyetnnuAQAAYHeZ9xmpn1m2/lOZ9Qq9dZ6Du/vyzHqPlnruCvu9MMkLd6YNAABgrcwVpLr70cvbquo+SR6y2ysCAABYcHM/I7WC98Q7nAAAgD3QvM9I/eyypp9I8rtJzt7tFQEAACy4eZ+R+kKSTlLT+g+SfDzJI1ejKAAAgEU27zNSuzIEEAAA4GpFQAIAABg07zNSZ2c2tG+HuvugXa4IAABgwc37jNSxmT0P9T+TnJXk4CSPT/J3SU5fndIAAAAW07xB6lFJ7t3dX9/aUFXvTvLP3f0Xq1EYAADAopr3GakbJbl4WdvFSW68e8sBAABYfPMGqbcneXtV3bOqbllV90ry1qkdAABgjzJvkHpsko8k+eskZyR5eZKPTu0AAAB7lHnfI7UlydOnDwAAwB5t7vdITcP6XlVV75jWN1fV3VevNAAAgMU0V5CqqidkNpzv80nuPDVfkuRPV6kuAACAhTVvj9STkxzZ3X+W5Mqp7TNJbrEaRQEAACyyeYPUfknOnpZ7+rl3kst2e0UAAAALbt4g9cH8+EQTT0zy/t1bDgAAwOKba9a+JE9I8o6qOjrJflX12STfS3K/VasMAABgQV1lkKqqayS5ZZI7JblVkoMzG+Z3andfuaNjAQAAro6uMkh195VVdXx375fk1OkDAACwx5r7Gamquv2qVgIAALBBzPuM1FlJ3l1Vx2c2rG/rzH3p7metRmEAAACLat4gdZ0kb5uWb7KkvX98VwAAgKu37Qapqnp8d79sWn1+d39hjWoCAABYaDt6Rur5S5bPWO1CAAAANoodDe37UlX9RZJ/T7J3VR210k7dfdyqVAYAALCgdhSkfjvJf0vykCR7J3n4Cvt0EkEKAADYo2w3SHX355L8XpJU1b929z3WrCoAAIAFNtd7pIQoAACAH5n3hbwAAABMBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABq1JkKqqG1XVGVW1par2mtpeWlUnVdWxS/bb6TYAAIC1slY9UucnuUeSU5Kkqm6TZN/uvlOSfarq8F1pW6N7AAAASJLstRYX6e4tSbZU1dam2yd577R8QpI7JPnhLrSdtorlAwAAbGO9npE6IMlF0/KF0/qutG2jqo6pqtOr6vTzzjtvtxYOAACwXkHqwiT7T8v7J7lgF9u20d2v6O7N3b1506ZNu714AABgz7ZeQeojmT0zlSRHZvbs1K60AQAArJm1mrVv76o6IclhSf4lyd6ZPTN1UpIruvvU7j5jZ9vW4h4AAAC2WqvJJi7PrPdoqY+usN+TdrYNAABgrXghLwAAwKA16ZECrr7OeeVvr3cJ7GF+6ug3rXcJAKBHCgAAYJQgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAxatyBVVYdU1blVdWJVvWdqe1pVnVxVr6+qvUfaAAAA1sp690i9t7vv2t33qqobJLlbdx+R5JNJHjhv23oVDwAA7JnWO0jdrapOqqqnJNmc5MSp/YQkdxho20ZVHVNVp1fV6eedd96qFQ8AAOyZ1jNIfTPJzZPcLcmRmQWki6ZtFyY5YPrM07aN7n5Fd2/u7s2bNm1ajdoBAIA92LoFqe6+tLu/390/TPLOJF9Msv+0ef8kF2QWlOZpAwAAWDPrOdnEfktWfzXJF5LcZVo/MskpSU6bsw0AAGDNrOfQvjtV1ceq6sNJvt7dH03ywao6Ocmtk7ytu781T9t6FA8AAOy59lqvC3f3PyX5p2VtL0zywp1pAwAAWCvrPWsfAADAhiNIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwaK/1LgAAri6OetNR610Ce6Djfvu49S4B9kh6pAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAoA0bpKrqpVV1UlUdu961AAAAe5YNGaSq6jZJ9u3uOyXZp6oOX++aAACAPcde613ATrp9kvdOyyckuUOS09avHAAAlvvY0cesdwnsYX7lla9Ys2tVd6/ZxXaXqvrjJGd09z9X1ZFJ7tjdz1uy/ZgkW39zb5Hks+tQJj/uwCTfXu8iYEH4fYBt+Z2AbfmdWAwHd/emlTZs1B6pC5PsPy3vn+SCpRu7+xVJ1i6OMpeqOr27N693HbAI/D7AtvxOwLb8Tiy+DfmMVJKPJLnHtHxkklPWsRYAAGAPsyGDVHefkWRLVZ2U5IruPnW9awIAAPYcG3VoX7r7SetdA8MMt4Qf8fsA2/I7AdvyO7HgNuRkEwAAAOtpQw7tAwAAWE+CFAAAwCBBijVRVS+tqpOq6tj1rgXWW1XdqKrOqKotVbVhn1WF3aGqbldVH66qk6vqpetdD6y3qjp0+p04qapeXVW13jWxMkGKVVdVt0myb3ffKck+VXX4etcE6+z8zF7h4NUNkJyV5O7dfUSSG1TVrda7IFhnn+3uO05/NyWJd0ktKEGKtXD7JO+dlk9Icod1rAXWXXdv6e7vrncdsAi6+5zu3jKtXp7kivWsB9Zbd1++ZPXSJGevVy3smCDFWjggyUXT8oXTOgD8h6r65SSbuvtT610LrLeqekBVnZnkhkm+s971sDJBirVwYZL9p+X9k1ywfqUAsGiq6npJXpbkMetdCyyC7n57dx+a5GtJ7rfe9bAyQYq18JHMngdJkiPjuRAAJtOEK69L8tTuPme964H1VlXXWrJ6UZJL1qsWdkyQYtV19xlJtlTVSUmu6O5T17smWE9VtXdVnZDksCT/UlW3W++aYB09OMnhSV5UVSdWledo2dPdp6o+UFUfyGxo33vWuyBWVt293jUAAABsKHqkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFwLqqqtdU1Z+u07Wrql5dVd+tqoV7NUNV/WZVnV1VF1fVf17vegD4EUEKgG1U1Veq6ltVdd0lbb9XVSeuY1mr5Ygk90xyk+6+7fKNVfWoqjp57cv6Dy9O8vju3re7P76OdQCwjCAFwEqumeRJ613EqKq65uAhByf5Snd/fw2vOeLgJP++MwdW1V67uRYAlhCkAFjJnyd5alUdsHxDVR1SVb30D/WqOrGqfm9aflRVfaiqXlpVF1TVl6rqjlP72VNv1yOXnfbAqnpvVX2vqj5QVQcvOfcvTNvOr6rPVtX/s2Tba6rq5VX1T1X1/SR3W6HeG1XV26fjv1BVR0/tj0nyt0nuMA2de+6y426Z5K+XbL9ge9esqvtW1cer6qLpHp+zwvf1yKr6alV9u6r+ZMn221bV6dOx51bVS6rqWlV1cWaB9hNV9cUl9/KWqjqvqr5cVU9ccp7nVNWbq+p1VXVRkketdO6V/7kBGCVIAbCS05OcmOSpO3n87ZJ8Msn1k7whyf9JcniSmyV5WJKXVdW+S/Z/aJL/nuTAJP+W5PVJMg0vfO90jhsk+Z0k/6uqfnHJsb+b5PlJ9kuy0jC8/5Pka0lulOS3krygqu7e3a9K8tgkH5mGzj176UHd/ell2w/YwTW/n+QRSQ5Ict8kf1BVD1xWxxFJbpHkHkmeNQW1JDk2ybHdvX+Sn0vy9919aXdv/X4O6+6fq6prJHlHkk8kufF0nidX1b2XXOM3krx5quP1K517he8HgJ0gSAGwPc9K8oSq2rQTx365u1/d3VckeVOSn0nyvCkgvCfJZZmFqq3e1d0f7O5Lk/xJZr1AP5PkfpkNvXt1d/9wek7oLUkevOTY47v7Q919ZXdvWVrEdI5fTfJH3b2lu/8ts16oR+zEPS21zTW7+8Tu/r/T+ieTvDHJXZYd89zuvqS7P5FZGDpsar88yc2q6sDuvri7T9nONQ9Psqm7n9fdl3X3l5K8MrNwudVHuvttUx2XDJwbgEGCFAAr6u4zk7wzydN34vBzlyxfMp1vedvSHqmzl1z34iTnZ9aDdHCS201DBC+Yhtc9NMlPrXTsCm6U5Pzu/t6StrMy69HZFdtcs6puV1Xvn4bcXZhZT9aBy445Z8nyD/Kj+39Mkpsn+UxVnVZV99vONQ9OcqNl38UfJ7nh9uoaODcAgzyICsCOPDvJGUn+Yknb1okZfiLJRdPy0mCzM35m68I05O96Sb6RWTD4QHffcwfH9g62fSPJ9apqvyVh6qAkX5+zru2de3n7G5K8LMmvdfeWqvrL/HiQWvlE3Z9P8pBp6N6Dkry5qq6/wgQYZ2fW0/fz89Y7cG4ABumRAmC7uvsLmQ3Ne+KStvMyCyIPq6prVtVRmT1/syt+vaqOqKp9MntW6pTuPjuzHrGbV9XDq2rv6XP4kueLrqr+s5N8OMn/qKprV9UvZ9ZL87o56zo3yU2munZkv8x6vrZU1W0ze4ZqLlX1sKra1N1XJrlgar5yhV1PTfK9qvqjqrrO9N0fWlWH74ZzAzBIkALgqjwvyXWXtR2d5GlJvpPklzILK7viDZn1fp2f5Fcym5AiUy/SvTJ7DugbmQ2Pe2GSaw2c+yFJDpmOf2uSZ3f3CXMe+77Mph8/p6q+vYP9HpfkeVX1vcyeLRuZ1OE+Sf59mqXv2CS/Mz3ftI3pebP7Jbl1ki8n+XZmz3v95K6eG4Bx1b2jEREAAAAsp0cKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMCg/x+nFt6WXJa04gAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -\tFrequency of 0, 1, 2 and 3+ transfers.\n",
    "# dfselected[\"nTransfers\"].value_counts(normalize=True).sort_values(ascending=False).plot(kind = 'barh')\n",
    "nTransfers = dfselected[\"nTransfers\"].value_counts()\n",
    "# dfsample\n",
    "nTransfers[3] = nTransfers[4] + nTransfers[3]\n",
    "nTransfers.pop(4)\n",
    "# nTransfers\n",
    "\n",
    "# nTransfers = dfselected[\"nTransfers\"].value_counts(normalize=True)\n",
    "fig = plt.figure(constrained_layout=True,figsize=(11.69,8.27))\n",
    "gs = GridSpec(7, 12, figure=fig)\n",
    "# set palette\n",
    "palette = itertools.cycle(sns.color_palette())\n",
    "# color\n",
    "c = next(palette)\n",
    "ax1 = fig.add_subplot(gs[:,:])\n",
    "sns.barplot(x=nTransfers.index, y=nTransfers.values, alpha=0.8)\n",
    "# sns.barplot(ax=ax1,data=nTransfers,x=(nTransfers.index),y=nTransfers.values,alpha=0.8,color=c)\n",
    "ax1.set_title('Total number of transfers',fontdict={\"size\":14})\n",
    "ax1.set_xlabel(xlabel=\"Number of transfers\",fontdict={\"size\":12})\n",
    "ax1.set_ylabel(ylabel=\"frequencies of transfer\",fontdict={\"size\":12})\n",
    "ax1.tick_params(labelsize=8)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [],
   "source": [
    "# Frequency of walk access distance to transit, in 200m bins\n",
    "dfaccess = dfselected.loc[dfselected[\"access_mode\"]==\"walk\"].copy()\n",
    "dfaccess.loc[:,\"1st_boarding_dist_ft\"] = dfaccess[\"orig_firstboard_dist\"]*5280\n",
    "dfaccess[[\"access_mode\",\"orig_dest_dist\",\"orig_firstboard_dist\",\"1st_boarding_dist_ft\"]].to_csv(BASE_DIR.parent.joinpath(\"Muni on-board\",\"Access Trip Distances.csv\"))\n",
    "dfegress = dfselected.loc[dfselected[\"egress_mode\"]==\"walk\"].copy()\n",
    "dfegress.loc[:,\"lst_egress_dist_ft\"] = dfegress[\"last_alight_dest_dist\"]*5280\n",
    "dfegress[[\"egress_mode\",\"orig_dest_dist\",\"last_alight_dest_dist\",\"lst_egress_dist_ft\"]].to_csv(BASE_DIR.parent.joinpath(\"Muni on-board\",\"Egress Trip Distances.csv\"))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='orig_firstboard_dist'>"
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x= dfaccess[\"orig_firstboard_dist\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='last_alight_dest_dist'>"
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x= dfegress[\"last_alight_dest_dist\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 841.68x595.44 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(constrained_layout=True,figsize=(11.69,8.27))\n",
    "gs = GridSpec(7, 12, figure=fig)\n",
    "# set palette\n",
    "palette = itertools.cycle(sns.color_palette())\n",
    "# color\n",
    "c = next(palette)\n",
    "ax1 = fig.add_subplot(gs[:,:])\n",
    "\n",
    "bins_range = range(round(min(dfaccess[\"1st_boarding_dist_ft\"])),round(max(dfaccess[\"1st_boarding_dist_ft\"])),4000)\n",
    "sns.histplot(ax=ax1,data=dfaccess,x=dfaccess[\"1st_boarding_dist_ft\"],stat='percent',bins=bins_range, color=c)\n",
    "ax1.set_title('Frequency of walk access distance to transit',fontdict={\"size\":14})\n",
    "ax1.set_xlabel(xlabel=\"Distance (feets)\",fontdict={\"size\":12})\n",
    "ax1.set_ylabel(ylabel=\"frequencies (%)\",fontdict={\"size\":12})\n",
    "ax1.tick_params(labelsize=8)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "outputs": [
    {
     "data": {
      "text/plain": "range(0, 5781, 200)"
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins_range = range(round(min(dfaccess[\"orig_firstboard_dist\"])),round(max(dfaccess[\"orig_firstboard_dist\"])),200)\n",
    "bins_range"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "outputs": [
    {
     "data": {
      "text/plain": "count    42975.000000\nmean         0.153082\nstd          0.222098\nmin          0.000306\n25%          0.037511\n50%          0.101068\n75%          0.192914\nmax         23.149718\nName: last_alight_dest_dist, dtype: float64"
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfegress[\"last_alight_dest_dist\"].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "count    42645.000000\nmean         0.291805\nstd         27.997328\nmin          0.000069\n25%          0.036595\n50%          0.096742\n75%          0.190421\nmax       5781.480376\nName: orig_firstboard_dist, dtype: float64"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfaccess[\"orig_firstboard_dist\"].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [
    {
     "data": {
      "text/plain": "          ID         operator access_mode          dest_purp egress_mode  \\\n1         10  SF Muni [LOCAL]        walk          escorting        walk   \n2        100  SF Muni [LOCAL]        walk               home        walk   \n3       1000  SF Muni [LOCAL]        walk  social recreation        walk   \n4      10000  SF Muni [LOCAL]        walk               home        walk   \n5      10001  SF Muni [LOCAL]        walk               work        walk   \n...      ...              ...         ...                ...         ...   \n43764   9995  SF Muni [LOCAL]        walk  social recreation        walk   \n43765   9996  SF Muni [LOCAL]        walk            eat out        walk   \n43766   9997  SF Muni [LOCAL]        walk  other maintenance        walk   \n43767   9998  SF Muni [LOCAL]        walk  social recreation        walk   \n43768   9999  SF Muni [LOCAL]        walk               home        walk   \n\n               orig_purp      workers          tour_purp  \\\n1                   home          one               work   \n2      other maintenance         zero  other maintenance   \n3                   home         zero  social recreation   \n4                   work          two               work   \n5                   home          two               work   \n...                  ...          ...                ...   \n43764               home          one  social recreation   \n43765               work          two               work   \n43766               home          one  other maintenance   \n43767               work          one               work   \n43768               work  six or more               work   \n\n                tour_purp_case  worker_numeric_cat  ... TRANSFER_TYPE  \\\n1      home origin, work after                   1  ...         LB_HR   \n2             origin to home w                   0  ...  NO_TRANSFERS   \n3       home to destination nw                   0  ...         LB_LB   \n4                 work to home                   2  ...  NO_TRANSFERS   \n5                 home to work                   2  ...  NO_TRANSFERS   \n...                        ...                 ...  ...           ...   \n43764   home to destination nw                   1  ...  NO_TRANSFERS   \n43765           default origin                   2  ...  NO_TRANSFERS   \n43766   home to destination nw                   1  ...  NO_TRANSFERS   \n43767           default origin                   1  ...  NO_TRANSFERS   \n43768             work to home                   4  ...         LB_HR   \n\n      final_tripWeight_2015 final_boardWeight_2015 final_expansionFactor  \\\n1                  7.917375              23.752124              0.884662   \n2                 25.771842              25.771842              0.992905   \n3                 12.264525              24.529051              0.937222   \n4                 13.749268              13.749268              0.992905   \n5                 13.749268              13.749268              0.992905   \n...                     ...                    ...                   ...   \n43764             19.280810              19.280810              0.992904   \n43765             19.280810              19.280810              0.992904   \n43766             19.280810              19.280810              0.992904   \n43767             13.749268              13.749268              0.992905   \n43768              6.489101              12.978202              0.937222   \n\n      orig_dest_dist orig_firstboard_dist orig_surveyboard_dist  \\\n1           5.433428             0.237783              6.058412   \n2           0.558603             0.092291              0.092291   \n3           1.576316             0.119062              0.119062   \n4           1.629737             0.066227              0.066227   \n5           1.553987             0.140475              0.140475   \n...              ...                  ...                   ...   \n43764       0.782256             0.101703              0.101703   \n43765       0.377551             0.037937              0.037937   \n43766       0.729543             0.046433              0.046433   \n43767       0.773978             0.015297              0.015297   \n43768      30.326860             0.056021              0.056021   \n\n      survey_alight_dest_dist last_alight_dest_dist  \\\n1                    0.007223              0.007223   \n2                    0.005086              0.005086   \n3                    0.393891              0.030224   \n4                    0.070712              0.070712   \n5                    0.023352              0.023352   \n...                       ...                   ...   \n43764                0.007420              0.007420   \n43765                0.005502              0.005502   \n43766                0.007420              0.007420   \n43767                0.032994              0.032994   \n43768               29.342951              1.153316   \n\n      orig_firstboard_dist_meters  \n1                           383.0  \n2                           149.0  \n3                           192.0  \n4                           107.0  \n5                           226.0  \n...                           ...  \n43764                       164.0  \n43765                        61.0  \n43766                        75.0  \n43767                        25.0  \n43768                        90.0  \n\n[42645 rows x 57 columns]",
      "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>ID</th>\n      <th>operator</th>\n      <th>access_mode</th>\n      <th>dest_purp</th>\n      <th>egress_mode</th>\n      <th>orig_purp</th>\n      <th>workers</th>\n      <th>tour_purp</th>\n      <th>tour_purp_case</th>\n      <th>worker_numeric_cat</th>\n      <th>...</th>\n      <th>TRANSFER_TYPE</th>\n      <th>final_tripWeight_2015</th>\n      <th>final_boardWeight_2015</th>\n      <th>final_expansionFactor</th>\n      <th>orig_dest_dist</th>\n      <th>orig_firstboard_dist</th>\n      <th>orig_surveyboard_dist</th>\n      <th>survey_alight_dest_dist</th>\n      <th>last_alight_dest_dist</th>\n      <th>orig_firstboard_dist_meters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>10</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>escorting</td>\n      <td>walk</td>\n      <td>home</td>\n      <td>one</td>\n      <td>work</td>\n      <td>home origin, work after</td>\n      <td>1</td>\n      <td>...</td>\n      <td>LB_HR</td>\n      <td>7.917375</td>\n      <td>23.752124</td>\n      <td>0.884662</td>\n      <td>5.433428</td>\n      <td>0.237783</td>\n      <td>6.058412</td>\n      <td>0.007223</td>\n      <td>0.007223</td>\n      <td>383.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>home</td>\n      <td>walk</td>\n      <td>other maintenance</td>\n      <td>zero</td>\n      <td>other maintenance</td>\n      <td>origin to home w</td>\n      <td>0</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>25.771842</td>\n      <td>25.771842</td>\n      <td>0.992905</td>\n      <td>0.558603</td>\n      <td>0.092291</td>\n      <td>0.092291</td>\n      <td>0.005086</td>\n      <td>0.005086</td>\n      <td>149.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>social recreation</td>\n      <td>walk</td>\n      <td>home</td>\n      <td>zero</td>\n      <td>social recreation</td>\n      <td>home to destination nw</td>\n      <td>0</td>\n      <td>...</td>\n      <td>LB_LB</td>\n      <td>12.264525</td>\n      <td>24.529051</td>\n      <td>0.937222</td>\n      <td>1.576316</td>\n      <td>0.119062</td>\n      <td>0.119062</td>\n      <td>0.393891</td>\n      <td>0.030224</td>\n      <td>192.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10000</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>home</td>\n      <td>walk</td>\n      <td>work</td>\n      <td>two</td>\n      <td>work</td>\n      <td>work to home</td>\n      <td>2</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>13.749268</td>\n      <td>13.749268</td>\n      <td>0.992905</td>\n      <td>1.629737</td>\n      <td>0.066227</td>\n      <td>0.066227</td>\n      <td>0.070712</td>\n      <td>0.070712</td>\n      <td>107.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>10001</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>work</td>\n      <td>walk</td>\n      <td>home</td>\n      <td>two</td>\n      <td>work</td>\n      <td>home to work</td>\n      <td>2</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>13.749268</td>\n      <td>13.749268</td>\n      <td>0.992905</td>\n      <td>1.553987</td>\n      <td>0.140475</td>\n      <td>0.140475</td>\n      <td>0.023352</td>\n      <td>0.023352</td>\n      <td>226.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>43764</th>\n      <td>9995</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>social recreation</td>\n      <td>walk</td>\n      <td>home</td>\n      <td>one</td>\n      <td>social recreation</td>\n      <td>home to destination nw</td>\n      <td>1</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>19.280810</td>\n      <td>19.280810</td>\n      <td>0.992904</td>\n      <td>0.782256</td>\n      <td>0.101703</td>\n      <td>0.101703</td>\n      <td>0.007420</td>\n      <td>0.007420</td>\n      <td>164.0</td>\n    </tr>\n    <tr>\n      <th>43765</th>\n      <td>9996</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>eat out</td>\n      <td>walk</td>\n      <td>work</td>\n      <td>two</td>\n      <td>work</td>\n      <td>default origin</td>\n      <td>2</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>19.280810</td>\n      <td>19.280810</td>\n      <td>0.992904</td>\n      <td>0.377551</td>\n      <td>0.037937</td>\n      <td>0.037937</td>\n      <td>0.005502</td>\n      <td>0.005502</td>\n      <td>61.0</td>\n    </tr>\n    <tr>\n      <th>43766</th>\n      <td>9997</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>other maintenance</td>\n      <td>walk</td>\n      <td>home</td>\n      <td>one</td>\n      <td>other maintenance</td>\n      <td>home to destination nw</td>\n      <td>1</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>19.280810</td>\n      <td>19.280810</td>\n      <td>0.992904</td>\n      <td>0.729543</td>\n      <td>0.046433</td>\n      <td>0.046433</td>\n      <td>0.007420</td>\n      <td>0.007420</td>\n      <td>75.0</td>\n    </tr>\n    <tr>\n      <th>43767</th>\n      <td>9998</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>social recreation</td>\n      <td>walk</td>\n      <td>work</td>\n      <td>one</td>\n      <td>work</td>\n      <td>default origin</td>\n      <td>1</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>13.749268</td>\n      <td>13.749268</td>\n      <td>0.992905</td>\n      <td>0.773978</td>\n      <td>0.015297</td>\n      <td>0.015297</td>\n      <td>0.032994</td>\n      <td>0.032994</td>\n      <td>25.0</td>\n    </tr>\n    <tr>\n      <th>43768</th>\n      <td>9999</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>walk</td>\n      <td>home</td>\n      <td>walk</td>\n      <td>work</td>\n      <td>six or more</td>\n      <td>work</td>\n      <td>work to home</td>\n      <td>4</td>\n      <td>...</td>\n      <td>LB_HR</td>\n      <td>6.489101</td>\n      <td>12.978202</td>\n      <td>0.937222</td>\n      <td>30.326860</td>\n      <td>0.056021</td>\n      <td>0.056021</td>\n      <td>29.342951</td>\n      <td>1.153316</td>\n      <td>90.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>42645 rows × 57 columns</p>\n</div>"
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure(constrained_layout=True,figsize=(11.69,8.27))\n",
    "gs = GridSpec(7, 12, figure=fig)\n",
    "# set palette\n",
    "palette = itertools.cycle(sns.color_palette())\n",
    "# color\n",
    "c = next(palette)\n",
    "ax1 = fig.add_subplot(gs[:,:])\n",
    "\n",
    "bins_range = range(round(min(dfegress[\"lst_egress_dist_ft\"])),round(max(dfegress[\"lst_egress_dist_ft\"])),200)\n",
    "sns.histplot(ax=ax1,data=dfaccess,x=dfegress[\"lst_egress_dist_ft\"],stat='percent',bins=bins_range, color=c)\n",
    "ax1.set_title('Frequency of walk egress distance to destination',fontdict={\"size\":14})\n",
    "ax1.set_xlabel(xlabel=\"Distance (feets)\",fontdict={\"size\":12})\n",
    "ax1.set_ylabel(ylabel=\"frequencies (%)\",fontdict={\"size\":12})\n",
    "ax1.tick_params(labelsize=8)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ID', 'operator', 'survey_year', 'SURVEY_MODE', 'access_mode', 'depart_hour', 'dest_purp', 'direction', 'egress_mode', 'eng_proficient', 'fare_category', 'fare_medium', 'gender', 'hispanic', 'household_income', 'interview_language', 'onoff_enter_station', 'onoff_exit_station', 'orig_purp', 'persons', 'return_hour', 'route', 'student_status', 'survey_type', 'time_period', 'transit_type', 'trip_purp', 'vehicles', 'weekpart', 'weight', 'work_status', 'workers', 'canonical_operator', 'operator_detail', 'technology', 'approximate_age', 'tour_purp', 'tour_purp_case', 'vehicle_numeric_cat', 'worker_numeric_cat', 'auto_suff', 'first_before_operator_detail', 'second_before_operator_detail', 'third_before_operator_detail', 'first_after_operator_detail', 'second_after_operator_detail', 'third_after_operator_detail', 'first_before_operator', 'second_before_operator', 'third_before_operator', 'first_after_operator', 'second_after_operator', 'third_after_operator', 'first_before_technology', 'second_before_technology', 'third_before_technology', 'first_after_technology', 'second_after_technology', 'third_after_technology', 'transfer_from', 'transfer_to', 'first_board_tech', 'last_alight_tech', 'commuter_rail_present', 'heavy_rail_present', 'express_bus_present', 'ferry_present', 'light_rail_present', 'boardings', 'race', 'language_at_home', 'day_of_the_week', 'field_start', 'field_end', 'day_part', 'unique_ID', 'trip_weight', 'field_language', 'survey_time', 'path_access', 'path_egress', 'path_line_haul', 'path_label', 'survey_batch', 'agg_tour_purp', 'access_mode_model', 'egress_mode_model', 'auto_suff_model', 'exp_factor', 'boardWeight_2015', 'tripWeight_2015', 'nTransfers', 'period', 'transfer_from_tech', 'transfer_to_tech', 'usedLB', 'usedCR', 'usedHR', 'usedEB', 'usedLR', 'usedFR', 'usedTotal', 'BEST_MODE', 'LB_CR', 'LB_HR', 'LB_LR', 'LB_FR', 'LB_EB', 'LB_LB', 'EB_CR', 'EB_HR', 'EB_LR', 'EB_FR', 'EB_EB', 'FR_CR', 'FR_HR', 'FR_LR', 'FR_FR', 'LR_CR', 'LR_HR', 'LR_LR', 'HR_CR', 'HR_HR', 'CR_CR', 'TRANSFER_TYPE', 'hh_id', 'final_tripWeight_2015', 'final_boardWeight_2015', 'final_expansionFactor', 'orig_dest_dist', 'orig_firstboard_dist', 'orig_surveyboard_dist', 'survey_alight_dest_dist', 'last_alight_dest_dist']\n"
     ]
    }
   ],
   "source": [
    "print(dfboardings.columns.to_list())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "          ID         operator  survey_year SURVEY_MODE access_mode  \\\n0          1  SF Muni [LOCAL]         2017          LB         knr   \n1         10  SF Muni [LOCAL]         2017          LB        walk   \n2        100  SF Muni [LOCAL]         2017          LB        walk   \n3       1000  SF Muni [LOCAL]         2017          LB        walk   \n4      10000  SF Muni [LOCAL]         2017          LB        walk   \n...      ...              ...          ...         ...         ...   \n43764   9995  SF Muni [LOCAL]         2017          LB        walk   \n43765   9996  SF Muni [LOCAL]         2017          LB        walk   \n43766   9997  SF Muni [LOCAL]         2017          LB        walk   \n43767   9998  SF Muni [LOCAL]         2017          LB        walk   \n43768   9999  SF Muni [LOCAL]         2017          LB        walk   \n\n       depart_hour          dest_purp  direction egress_mode eng_proficient  \\\n0              NaN               work        NaN        walk            NaN   \n1              NaN          escorting        NaN        walk      VERY WELL   \n2              8.0               home        NaN        walk            NaN   \n3              NaN  social recreation        NaN        walk       NOT WELL   \n4             11.0               home        NaN        walk      VERY WELL   \n...            ...                ...        ...         ...            ...   \n43764          NaN  social recreation        NaN        walk            NaN   \n43765          NaN            eat out        NaN        walk            NaN   \n43766          NaN  other maintenance        NaN        walk            NaN   \n43767          NaN  social recreation        NaN        walk            NaN   \n43768          NaN               home        NaN        walk            NaN   \n\n       ... TRANSFER_TYPE   hh_id final_tripWeight_2015 final_boardWeight_2015  \\\n0      ...         LB_HR   69386             12.581647              25.163294   \n1      ...         LB_HR   69387              7.917375              23.752124   \n2      ...  NO_TRANSFERS   69388             25.771842              25.771842   \n3      ...         LB_LB   69389             12.264525              24.529051   \n4      ...  NO_TRANSFERS   69390             13.749268              13.749268   \n...    ...           ...     ...                   ...                    ...   \n43764  ...  NO_TRANSFERS  113150             19.280810              19.280810   \n43765  ...  NO_TRANSFERS  113151             19.280810              19.280810   \n43766  ...  NO_TRANSFERS  113152             19.280810              19.280810   \n43767  ...  NO_TRANSFERS  113153             13.749268              13.749268   \n43768  ...         LB_HR  113154              6.489101              12.978202   \n\n      final_expansionFactor orig_dest_dist  orig_firstboard_dist  \\\n0                  0.937222       9.355325              1.007234   \n1                  0.884662       5.433428              0.237783   \n2                  0.992905       0.558603              0.092291   \n3                  0.937222       1.576316              0.119062   \n4                  0.992905       1.629737              0.066227   \n...                     ...            ...                   ...   \n43764              0.992904       0.782256              0.101703   \n43765              0.992904       0.377551              0.037937   \n43766              0.992904       0.729543              0.046433   \n43767              0.992905       0.773978              0.015297   \n43768              0.937222      30.326860              0.056021   \n\n       orig_surveyboard_dist survey_alight_dest_dist last_alight_dest_dist  \n0                   9.321404                0.293603              0.293603  \n1                   6.058412                0.007223              0.007223  \n2                   0.092291                0.005086              0.005086  \n3                   0.119062                0.393891              0.030224  \n4                   0.066227                0.070712              0.070712  \n...                      ...                     ...                   ...  \n43764               0.101703                0.007420              0.007420  \n43765               0.037937                0.005502              0.005502  \n43766               0.046433                0.007420              0.007420  \n43767               0.015297                0.032994              0.032994  \n43768               0.056021               29.342951              1.153316  \n\n[43769 rows x 134 columns]",
      "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>ID</th>\n      <th>operator</th>\n      <th>survey_year</th>\n      <th>SURVEY_MODE</th>\n      <th>access_mode</th>\n      <th>depart_hour</th>\n      <th>dest_purp</th>\n      <th>direction</th>\n      <th>egress_mode</th>\n      <th>eng_proficient</th>\n      <th>...</th>\n      <th>TRANSFER_TYPE</th>\n      <th>hh_id</th>\n      <th>final_tripWeight_2015</th>\n      <th>final_boardWeight_2015</th>\n      <th>final_expansionFactor</th>\n      <th>orig_dest_dist</th>\n      <th>orig_firstboard_dist</th>\n      <th>orig_surveyboard_dist</th>\n      <th>survey_alight_dest_dist</th>\n      <th>last_alight_dest_dist</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>knr</td>\n      <td>NaN</td>\n      <td>work</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>LB_HR</td>\n      <td>69386</td>\n      <td>12.581647</td>\n      <td>25.163294</td>\n      <td>0.937222</td>\n      <td>9.355325</td>\n      <td>1.007234</td>\n      <td>9.321404</td>\n      <td>0.293603</td>\n      <td>0.293603</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>escorting</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>VERY WELL</td>\n      <td>...</td>\n      <td>LB_HR</td>\n      <td>69387</td>\n      <td>7.917375</td>\n      <td>23.752124</td>\n      <td>0.884662</td>\n      <td>5.433428</td>\n      <td>0.237783</td>\n      <td>6.058412</td>\n      <td>0.007223</td>\n      <td>0.007223</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>walk</td>\n      <td>8.0</td>\n      <td>home</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>69388</td>\n      <td>25.771842</td>\n      <td>25.771842</td>\n      <td>0.992905</td>\n      <td>0.558603</td>\n      <td>0.092291</td>\n      <td>0.092291</td>\n      <td>0.005086</td>\n      <td>0.005086</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>social recreation</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>NOT WELL</td>\n      <td>...</td>\n      <td>LB_LB</td>\n      <td>69389</td>\n      <td>12.264525</td>\n      <td>24.529051</td>\n      <td>0.937222</td>\n      <td>1.576316</td>\n      <td>0.119062</td>\n      <td>0.119062</td>\n      <td>0.393891</td>\n      <td>0.030224</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10000</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>walk</td>\n      <td>11.0</td>\n      <td>home</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>VERY WELL</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>69390</td>\n      <td>13.749268</td>\n      <td>13.749268</td>\n      <td>0.992905</td>\n      <td>1.629737</td>\n      <td>0.066227</td>\n      <td>0.066227</td>\n      <td>0.070712</td>\n      <td>0.070712</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>43764</th>\n      <td>9995</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>social recreation</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>113150</td>\n      <td>19.280810</td>\n      <td>19.280810</td>\n      <td>0.992904</td>\n      <td>0.782256</td>\n      <td>0.101703</td>\n      <td>0.101703</td>\n      <td>0.007420</td>\n      <td>0.007420</td>\n    </tr>\n    <tr>\n      <th>43765</th>\n      <td>9996</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>eat out</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>113151</td>\n      <td>19.280810</td>\n      <td>19.280810</td>\n      <td>0.992904</td>\n      <td>0.377551</td>\n      <td>0.037937</td>\n      <td>0.037937</td>\n      <td>0.005502</td>\n      <td>0.005502</td>\n    </tr>\n    <tr>\n      <th>43766</th>\n      <td>9997</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>other maintenance</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>113152</td>\n      <td>19.280810</td>\n      <td>19.280810</td>\n      <td>0.992904</td>\n      <td>0.729543</td>\n      <td>0.046433</td>\n      <td>0.046433</td>\n      <td>0.007420</td>\n      <td>0.007420</td>\n    </tr>\n    <tr>\n      <th>43767</th>\n      <td>9998</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>social recreation</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NO_TRANSFERS</td>\n      <td>113153</td>\n      <td>13.749268</td>\n      <td>13.749268</td>\n      <td>0.992905</td>\n      <td>0.773978</td>\n      <td>0.015297</td>\n      <td>0.015297</td>\n      <td>0.032994</td>\n      <td>0.032994</td>\n    </tr>\n    <tr>\n      <th>43768</th>\n      <td>9999</td>\n      <td>SF Muni [LOCAL]</td>\n      <td>2017</td>\n      <td>LB</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>home</td>\n      <td>NaN</td>\n      <td>walk</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>LB_HR</td>\n      <td>113154</td>\n      <td>6.489101</td>\n      <td>12.978202</td>\n      <td>0.937222</td>\n      <td>30.326860</td>\n      <td>0.056021</td>\n      <td>0.056021</td>\n      <td>29.342951</td>\n      <td>1.153316</td>\n    </tr>\n  </tbody>\n</table>\n<p>43769 rows × 134 columns</p>\n</div>"
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