{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How Many Transit Riders in California have Autos?\n",
"\n",
"Questioner: Gillian Gillett \n",
"March 23, 2022"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup Environment\n",
"\n",
"! Warning: will install libraries into current environment."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:test\n"
]
}
],
"source": [
"import logging\n",
"\n",
"logging.basicConfig(level=logging.DEBUG)\n",
"logging.debug(\"test\")"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" import pandas as pd\n",
" import seaborn as sns\n",
" import matplotlib.pyplot as plt\n",
"except:\n",
" logging.info('pandas seaborn not found. Will try and install into current environment')\n",
" ! conda install pandas seaborn \n",
" import pandas as pd\n",
" import seaborn as sns\n",
" import matplotlib.pyplot as plt\n",
"\n",
"pd.set_option(\"display.max.columns\", None)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:Working directory: /Users/elizabeth/Documents/urbanlabs/CA_Interoperable/working/data-analyses\n"
]
}
],
"source": [
"import os\n",
"WORKING_DIR = os.path.dirname(os.getcwd())\n",
"\n",
"logging.info(f\"Working directory: {WORKING_DIR}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## National Household Travel Survey\n",
"\n",
"Data Codebook: [https://nhts.ornl.gov/tables09/CodebookBrowser.aspx](https://nhts.ornl.gov/tables09/CodebookBrowser.aspx)\n",
"\n",
"Relevant variables:\n",
"\n",
"- HOUSEID\tHousehold Identifier \n",
"- HBHUR\tUrban / Rural indicator - Block group \n",
"- HHFAMINC\tHousehold income \n",
"- HHSIZE\tCount of household members \n",
"- HHSTATE\tHousehold state\n",
"- HHVEHCNT\tCount of Household vehicles\n",
"- WRKCOUNT\tNumber of workers in household\n",
"- WTHHFIN\tFinal HH weight\n",
"\n",
"- CAR\tFrequency of Personal Vehicle Use for Travel\n",
"- BUS\tFrequency of Bus Use for Travel\n",
"- PARA\tFrequency of Paratransit Use for Travel\n",
"- TAXI\tFrequency of Taxi Service or Rideshare Use for Travel\n",
"- [WALK](https://nhts.ornl.gov/tables09/CodebookPage.aspx?id=1365) Frequency of Walk Use for Travel\n",
"- TRAIN\tFrequency of Train Use for Travel\n",
"\n",
"- PLACE\tTravel is a Financial Burden\n",
"- PTRANS\tPublic Transportation to Reduce Financial Burden of Travel\n",
"- WALK2SAVE\tWalk to Reduce Financial Burden of Travel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Data\n",
"\n",
"Assumes you have downloaded and unzipped NHTS data and weights into `csv` and `ReplicatesCSV` folders respectfully."
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:NHTS Data directory: /Users/elizabeth/Documents/urbanlabs/CA_Interoperable/working/NHTS\n"
]
}
],
"source": [
"NHTS_DATA_DIR = os.path.join(os.path.dirname(WORKING_DIR), 'NHTS')\n",
"logging.info(f\"NHTS Data directory: {NHTS_DATA_DIR}\")"
]
},
{
"cell_type": "code",
"execution_count": 81,
"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>HOUSEID</th>\n",
" <th>HHSIZE</th>\n",
" <th>HHVEHCNT</th>\n",
" <th>HHFAMINC</th>\n",
" <th>BUS</th>\n",
" <th>TRAIN</th>\n",
" <th>PLACE</th>\n",
" <th>HHSTATE</th>\n",
" <th>WTHHFIN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>30000041</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>CA</td>\n",
" <td>788.614240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>30000085</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>CA</td>\n",
" <td>190.669041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>30000094</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>CA</td>\n",
" <td>163.382292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>30000155</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>-7</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>CA</td>\n",
" <td>120.772451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>30000227</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>-9</td>\n",
" <td>-9</td>\n",
" <td>2</td>\n",
" <td>CA</td>\n",
" <td>62.015790</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HOUSEID HHSIZE HHVEHCNT HHFAMINC BUS TRAIN PLACE HHSTATE \\\n",
"6 30000041 2 2 11 4 4 5 CA \n",
"9 30000085 1 2 9 5 5 3 CA \n",
"11 30000094 1 1 4 5 5 2 CA \n",
"19 30000155 1 2 -7 5 4 2 CA \n",
"23 30000227 2 2 6 -9 -9 2 CA \n",
"\n",
" WTHHFIN \n",
"6 788.614240 \n",
"9 190.669041 \n",
"11 163.382292 \n",
"19 120.772451 \n",
"23 62.015790 "
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols_to_keep = [\n",
" 'HOUSEID',\n",
" 'HHSIZE',\n",
" 'HHVEHCNT',\n",
" 'HHFAMINC',\n",
" 'BUS',\n",
" 'TRAIN',\n",
" 'WTHHFIN',\n",
" 'PLACE',\n",
" 'HHSTATE',\n",
"]\n",
"\n",
"hh_all_df = pd.read_csv(\n",
" os.path.join(NHTS_DATA_DIR, 'csv','hhpub.csv'),\n",
" usecols=cols_to_keep,\n",
")\n",
"hh_all_df = hh_all_df[hh_all_df['HHSTATE'] == 'CA']\n",
"\n",
"hh_all_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analyze NHTS Data\n",
"\n",
"#### Recode Variables"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [],
"source": [
"def is_transit_user(x):\n",
" _FREQ_TRANSIT = [1,2,3]\n",
" _INFREQ_TRANSIT = [4,5]\n",
"\n",
" # UNKOWN\n",
" if int(x['BUS']) < 0 or int(x['TRAIN']) < 0:\n",
" return \"Unknown\"\n",
" # NO\n",
" if int(x['BUS']) == 5 and int(x['TRAIN']) == 5:\n",
" return \"No Transit Use\"\n",
" # YES\n",
" if int(x['BUS']) in _FREQ_TRANSIT or int(x['TRAIN']) in _FREQ_TRANSIT:\n",
" return \"Frequent Transit\"\n",
" if int(x['BUS']) in _FREQ_TRANSIT+_INFREQ_TRANSIT or int(x['TRAIN']) in _FREQ_TRANSIT+_INFREQ_TRANSIT:\n",
" return \"Infrequent Transit\"\n",
" else:\n",
" logging.debug(f\"Unable to process row for is_transit_user:\\n {x}\")\n",
" raise Exception(f'Unable to determine if transit user for row: {x}')\n",
"\n",
"def has_hh_veh(x):\n",
" # UNKOWN\n",
" if int(x['HHVEHCNT']) < 0:\n",
" return \"Unknown\"\n",
" # NO\n",
" if int(x['HHVEHCNT']) == 0:\n",
" return \"No Vehicle\"\n",
" # YES\n",
" if int(x['HHVEHCNT']) > 0:\n",
" return \"Has vehicle\"\n",
" else:\n",
" logging.debug(f\"Unable to process row for has_hh_veh:\\n {x}\")\n",
" raise Exception(f'Unable to determine if household has vehicles for row: {x}')\n",
"\n",
"def travel_burden_hh(x):\n",
" BURDEN = [1,2]\n",
" NOT_BURDEN = [3,4,5]\n",
" # UNKOWN\n",
" if int(x['PLACE']) < 0:\n",
" return \"Unknown\"\n",
" # NO\n",
" if int(x['PLACE']) in NOT_BURDEN:\n",
" return \"Not Burdened\"\n",
" # YES\n",
" if int(x['PLACE']) in BURDEN:\n",
" return \"Burdened\"\n",
" else:\n",
" logging.debug(f\"Unable to process row for travel_burden_hh:\\n {x}\")\n",
" raise Exception(f'Unable to determine if household has financial burden to travel for row: {x}')\n",
" \n",
"def filter_recs(x):\n",
" FILTER_COLS = ['transit_hh','vehicle_hh','burden_hh']\n",
" FILTER_VALUE = \"Unknown\"\n",
" if FILTER_VALUE in [x[c] for c in FILTER_COLS]:\n",
" return -1\n",
" return 1"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:Excluded records:\n",
" transit_hh vehicle_hh burden_hh\n",
"23 Unknown Has vehicle Burdened\n",
"106 Unknown Has vehicle Unknown\n",
"141 Unknown Has vehicle Not Burdened\n",
"211 Unknown Has vehicle Unknown\n",
"221 Unknown Has vehicle Unknown\n",
"/var/folders/60/xd2kny110pxfz3ln611jq7hm0000gn/T/ipykernel_8795/143102953.py:12: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" hh_df[c]= pd.Categorical(\n",
"DEBUG:root:Cleaned records:\n",
" transit_hh vehicle_hh burden_hh\n",
"6 Infrequent Transit Has vehicle Not Burdened\n",
"9 No Transit Use Has vehicle Not Burdened\n",
"11 No Transit Use Has vehicle Burdened\n",
"19 Infrequent Transit Has vehicle Burdened\n",
"37 No Transit Use Has vehicle Burdened\n",
"INFO:root:Cleaned records filtered to exclude 2927(11.2%) of 26099 records\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>HOUSEID</th>\n",
" <th>HHSIZE</th>\n",
" <th>HHVEHCNT</th>\n",
" <th>HHFAMINC</th>\n",
" <th>BUS</th>\n",
" <th>TRAIN</th>\n",
" <th>PLACE</th>\n",
" <th>HHSTATE</th>\n",
" <th>WTHHFIN</th>\n",
" <th>transit_hh</th>\n",
" <th>vehicle_hh</th>\n",
" <th>burden_hh</th>\n",
" <th>keep</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>30000041</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>CA</td>\n",
" <td>788.614240</td>\n",
" <td>Infrequent Transit</td>\n",
" <td>Has vehicle</td>\n",
" <td>Not Burdened</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>30000085</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>CA</td>\n",
" <td>190.669041</td>\n",
" <td>No Transit Use</td>\n",
" <td>Has vehicle</td>\n",
" <td>Not Burdened</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>30000094</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>CA</td>\n",
" <td>163.382292</td>\n",
" <td>No Transit Use</td>\n",
" <td>Has vehicle</td>\n",
" <td>Burdened</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>30000155</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>-7</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>CA</td>\n",
" <td>120.772451</td>\n",
" <td>Infrequent Transit</td>\n",
" <td>Has vehicle</td>\n",
" <td>Burdened</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>30000227</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>-9</td>\n",
" <td>-9</td>\n",
" <td>2</td>\n",
" <td>CA</td>\n",
" <td>62.015790</td>\n",
" <td>Unknown</td>\n",
" <td>Has vehicle</td>\n",
" <td>Burdened</td>\n",
" <td>-1</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",
" </tr>\n",
" <tr>\n",
" <th>129679</th>\n",
" <td>40794135</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>CA</td>\n",
" <td>63.217848</td>\n",
" <td>No Transit Use</td>\n",
" <td>Has vehicle</td>\n",
" <td>Not Burdened</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129682</th>\n",
" <td>40794179</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>CA</td>\n",
" <td>377.126813</td>\n",
" <td>No Transit Use</td>\n",
" <td>Has vehicle</td>\n",
" <td>Burdened</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129685</th>\n",
" <td>40794233</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>CA</td>\n",
" <td>33.421852</td>\n",
" <td>No Transit Use</td>\n",
" <td>Has vehicle</td>\n",
" <td>Not Burdened</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129691</th>\n",
" <td>40794291</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>CA</td>\n",
" <td>41.869638</td>\n",
" <td>No Transit Use</td>\n",
" <td>Has vehicle</td>\n",
" <td>Not Burdened</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129693</th>\n",
" <td>40794294</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>CA</td>\n",
" <td>207.672765</td>\n",
" <td>No Transit Use</td>\n",
" <td>Has vehicle</td>\n",
" <td>Not Burdened</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>26099 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" HOUSEID HHSIZE HHVEHCNT HHFAMINC BUS TRAIN PLACE HHSTATE \\\n",
"6 30000041 2 2 11 4 4 5 CA \n",
"9 30000085 1 2 9 5 5 3 CA \n",
"11 30000094 1 1 4 5 5 2 CA \n",
"19 30000155 1 2 -7 5 4 2 CA \n",
"23 30000227 2 2 6 -9 -9 2 CA \n",
"... ... ... ... ... ... ... ... ... \n",
"129679 40794135 2 3 5 5 5 3 CA \n",
"129682 40794179 1 1 6 5 5 2 CA \n",
"129685 40794233 2 3 8 5 5 3 CA \n",
"129691 40794291 1 1 9 5 5 5 CA \n",
"129693 40794294 2 2 10 5 5 4 CA \n",
"\n",
" WTHHFIN transit_hh vehicle_hh burden_hh keep \n",
"6 788.614240 Infrequent Transit Has vehicle Not Burdened 1 \n",
"9 190.669041 No Transit Use Has vehicle Not Burdened 1 \n",
"11 163.382292 No Transit Use Has vehicle Burdened 1 \n",
"19 120.772451 Infrequent Transit Has vehicle Burdened 1 \n",
"23 62.015790 Unknown Has vehicle Burdened -1 \n",
"... ... ... ... ... ... \n",
"129679 63.217848 No Transit Use Has vehicle Not Burdened 1 \n",
"129682 377.126813 No Transit Use Has vehicle Burdened 1 \n",
"129685 33.421852 No Transit Use Has vehicle Not Burdened 1 \n",
"129691 41.869638 No Transit Use Has vehicle Not Burdened 1 \n",
"129693 207.672765 No Transit Use Has vehicle Not Burdened 1 \n",
"\n",
"[26099 rows x 13 columns]"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_cols = ['transit_hh','vehicle_hh','burden_hh']\n",
"hh_all_df['transit_hh'] = hh_all_df.apply(lambda x: is_transit_user(x), axis=1)\n",
"hh_all_df['vehicle_hh'] = hh_all_df.apply(lambda x: has_hh_veh(x), axis=1)\n",
"hh_all_df['burden_hh'] = hh_all_df.apply(lambda x: travel_burden_hh(x), axis=1)\n",
"hh_all_df['keep'] = hh_all_df.apply(lambda x: filter_recs(x) ,axis=1)\n",
"\n",
"logging.debug(f\"Excluded records:\\n{hh_all_df[hh_all_df['keep']<0][analysis_cols].head()}\")\n",
"\n",
"hh_df = hh_all_df[hh_all_df['keep']>0]\n",
"\n",
"for c in analysis_cols:\n",
" hh_df[c]= pd.Categorical(\n",
" hh_df[c],\n",
" ordered = True,\n",
" )\n",
" \n",
"logging.debug(f\"Cleaned records:\\n{hh_df[analysis_cols].head()}\")\n",
"\n",
"recs_exc = len(hh_all_df)-len(hh_df)\n",
"logging.info(f\"Cleaned records filtered to exclude {recs_exc}({round(100*recs_exc/len(hh_all_df),1)}%) of {len(hh_all_df)} records\")\n",
"hh_all_df\n"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [
{
"data": {
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" color: #f1f1f1;\n",
"}\n",
"#T_e2db7_row0_col2, #T_e2db7_row2_col1, #T_e2db7_row2_col2, #T_e2db7_row3_col0 {\n",
" background-color: #023858;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_e2db7_row1_col0 {\n",
" background-color: #023b5d;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_e2db7_row1_col1 {\n",
" background-color: #f7f0f7;\n",
" color: #000000;\n",
"}\n",
"#T_e2db7_row1_col2, #T_e2db7_row2_col0, #T_e2db7_row3_col1 {\n",
" background-color: #fff7fb;\n",
" color: #000000;\n",
"}\n",
"#T_e2db7_row3_col2 {\n",
" background-color: #fdf5fa;\n",
" color: #000000;\n",
"}\n",
"#T_e2db7_row4_col0 {\n",
" background-color: #f3edf5;\n",
" color: #000000;\n",
"}\n",
"#T_e2db7_row4_col1 {\n",
" background-color: #045483;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_e2db7_row4_col2 {\n",
" background-color: #034973;\n",
" color: #f1f1f1;\n",
"}\n",
"</style>\n",
"<table id=\"T_e2db7\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank\" > </th>\n",
" <th class=\"index_name level0\" >HHold Transit Use in California (Source: NHTS 2017)</th>\n",
" <th id=\"T_e2db7_level0_col0\" class=\"col_heading level0 col0\" >Frequent Transit</th>\n",
" <th id=\"T_e2db7_level0_col1\" class=\"col_heading level0 col1\" >Infrequent Transit</th>\n",
" <th id=\"T_e2db7_level0_col2\" class=\"col_heading level0 col2\" >No Transit Use</th>\n",
" </tr>\n",
" <tr>\n",
" <th class=\"index_name level0\" >HHold Transportation Burden</th>\n",
" <th class=\"index_name level1\" >HHold Vehicles</th>\n",
" <th class=\"blank col0\" > </th>\n",
" <th class=\"blank col1\" > </th>\n",
" <th class=\"blank col2\" > </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_e2db7_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"2\">Burdened</th>\n",
" <th id=\"T_e2db7_level1_row0\" class=\"row_heading level1 row0\" >Has vehicle</th>\n",
" <td id=\"T_e2db7_row0_col0\" class=\"data row0 col0\" >17.7%</td>\n",
" <td id=\"T_e2db7_row0_col1\" class=\"data row0 col1\" >31.0%</td>\n",
" <td id=\"T_e2db7_row0_col2\" class=\"data row0 col2\" >51.3%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e2db7_level1_row1\" class=\"row_heading level1 row1\" >No Vehicle</th>\n",
" <td id=\"T_e2db7_row1_col0\" class=\"data row1 col0\" >76.1%</td>\n",
" <td id=\"T_e2db7_row1_col1\" class=\"data row1 col1\" >9.2%</td>\n",
" <td id=\"T_e2db7_row1_col2\" class=\"data row1 col2\" >14.7%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e2db7_level0_row2\" class=\"row_heading level0 row2\" rowspan=\"2\">Not Burdened</th>\n",
" <th id=\"T_e2db7_level1_row2\" class=\"row_heading level1 row2\" >Has vehicle</th>\n",
" <td id=\"T_e2db7_row2_col0\" class=\"data row2 col0\" >15.3%</td>\n",
" <td id=\"T_e2db7_row2_col1\" class=\"data row2 col1\" >33.3%</td>\n",
" <td id=\"T_e2db7_row2_col2\" class=\"data row2 col2\" >51.4%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e2db7_level1_row3\" class=\"row_heading level1 row3\" >No Vehicle</th>\n",
" <td id=\"T_e2db7_row3_col0\" class=\"data row3 col0\" >77.0%</td>\n",
" <td id=\"T_e2db7_row3_col1\" class=\"data row3 col1\" >7.8%</td>\n",
" <td id=\"T_e2db7_row3_col2\" class=\"data row3 col2\" >15.3%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e2db7_level0_row4\" class=\"row_heading level0 row4\" >All</th>\n",
" <th id=\"T_e2db7_level1_row4\" class=\"row_heading level1 row4\" ></th>\n",
" <td id=\"T_e2db7_row4_col0\" class=\"data row4 col0\" >20.3%</td>\n",
" <td id=\"T_e2db7_row4_col1\" class=\"data row4 col1\" >30.7%</td>\n",
" <td id=\"T_e2db7_row4_col2\" class=\"data row4 col2\" >49.0%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x169f90220>"
]
},
"execution_count": 194,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hh_df_3way_ct = pd.crosstab(\n",
" [hh_df['burden_hh'],hh_df['vehicle_hh']],\n",
" hh_df['transit_hh'], \n",
" values = hh_df['WTHHFIN'], \n",
" aggfunc = sum,\n",
" normalize='index',\n",
" margins = True,\n",
" rownames=['HHold Transportation Burden','HHold Vehicles'], \n",
" colnames=['HHold Transit Use in California (Source: NHTS 2017)'],\n",
")\n",
"s_hh_df_3way_ct=hh_df_3way_ct.style\\\n",
" .background_gradient()\\\n",
" .format(\"{:.1%}\")\n",
" \n",
"s_hh_df_3way_ct"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_8780b_row0_col0, #T_8780b_row1_col1, #T_8780b_row1_col2 {\n",
" background-color: #fff7fb;\n",
" color: #000000;\n",
"}\n",
"#T_8780b_row0_col1, #T_8780b_row0_col2, #T_8780b_row1_col0 {\n",
" background-color: #023858;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_8780b_row2_col0 {\n",
" background-color: #f5eff6;\n",
" color: #000000;\n",
"}\n",
"#T_8780b_row2_col1, #T_8780b_row2_col2 {\n",
" background-color: #034973;\n",
" color: #f1f1f1;\n",
"}\n",
"</style>\n",
"<table id=\"T_8780b\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"index_name level0\" >HHold Transit Use in California (Source: NHTS 2017)</th>\n",
" <th id=\"T_8780b_level0_col0\" class=\"col_heading level0 col0\" >Frequent Transit</th>\n",
" <th id=\"T_8780b_level0_col1\" class=\"col_heading level0 col1\" >Infrequent Transit</th>\n",
" <th id=\"T_8780b_level0_col2\" class=\"col_heading level0 col2\" >No Transit Use</th>\n",
" </tr>\n",
" <tr>\n",
" <th class=\"index_name level0\" >HHold Vehicles</th>\n",
" <th class=\"blank col0\" > </th>\n",
" <th class=\"blank col1\" > </th>\n",
" <th class=\"blank col2\" > </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_8780b_level0_row0\" class=\"row_heading level0 row0\" >Has vehicle</th>\n",
" <td id=\"T_8780b_row0_col0\" class=\"data row0 col0\" >16.3%</td>\n",
" <td id=\"T_8780b_row0_col1\" class=\"data row0 col1\" >32.3%</td>\n",
" <td id=\"T_8780b_row0_col2\" class=\"data row0 col2\" >51.4%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_8780b_level0_row1\" class=\"row_heading level0 row1\" >No Vehicle</th>\n",
" <td id=\"T_8780b_row1_col0\" class=\"data row1 col0\" >76.5%</td>\n",
" <td id=\"T_8780b_row1_col1\" class=\"data row1 col1\" >8.5%</td>\n",
" <td id=\"T_8780b_row1_col2\" class=\"data row1 col2\" >15.0%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_8780b_level0_row2\" class=\"row_heading level0 row2\" >All</th>\n",
" <td id=\"T_8780b_row2_col0\" class=\"data row2 col0\" >20.3%</td>\n",
" <td id=\"T_8780b_row2_col1\" class=\"data row2 col1\" >30.7%</td>\n",
" <td id=\"T_8780b_row2_col2\" class=\"data row2 col2\" >49.0%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x16a858460>"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hh_df_ct = pd.crosstab(\n",
" hh_df['vehicle_hh'],\n",
" hh_df['transit_hh'], \n",
" values = hh_df['WTHHFIN'], \n",
" aggfunc = sum,\n",
" normalize='index',\n",
" margins = True,\n",
" rownames=['HHold Vehicles'], \n",
" colnames=['HHold Transit Use in California (Source: NHTS 2017)'],\n",
")\n",
"s_hh_df_ct=hh_df_ct.style\\\n",
" .background_gradient()\\\n",
" .format(\"{:.1%}\")\n",
"\n",
"s_hh_df_ct"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_9b521_row0_col0, #T_9b521_row1_col1 {\n",
" background-color: #fff7fb;\n",
" color: #000000;\n",
"}\n",
"#T_9b521_row0_col1, #T_9b521_row1_col0 {\n",
" background-color: #023858;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_9b521_row2_col0 {\n",
" background-color: #023a5b;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_9b521_row2_col1 {\n",
" background-color: #fef6fa;\n",
" color: #000000;\n",
"}\n",
"#T_9b521_row3_col0 {\n",
" background-color: #0568a3;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_9b521_row3_col1 {\n",
" background-color: #dad9ea;\n",
" color: #000000;\n",
"}\n",
"</style>\n",
"<table id=\"T_9b521\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"index_name level0\" >HHold Vehicles</th>\n",
" <th id=\"T_9b521_level0_col0\" class=\"col_heading level0 col0\" >Has vehicle</th>\n",
" <th id=\"T_9b521_level0_col1\" class=\"col_heading level0 col1\" >No Vehicle</th>\n",
" </tr>\n",
" <tr>\n",
" <th class=\"index_name level0\" >HHold Transit Use in California (Source: NHTS 2017)</th>\n",
" <th class=\"blank col0\" > </th>\n",
" <th class=\"blank col1\" > </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_9b521_level0_row0\" class=\"row_heading level0 row0\" >Frequent Transit</th>\n",
" <td id=\"T_9b521_row0_col0\" class=\"data row0 col0\" >75.2%</td>\n",
" <td id=\"T_9b521_row0_col1\" class=\"data row0 col1\" >24.8%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_9b521_level0_row1\" class=\"row_heading level0 row1\" >Infrequent Transit</th>\n",
" <td id=\"T_9b521_row1_col0\" class=\"data row1 col0\" >98.2%</td>\n",
" <td id=\"T_9b521_row1_col1\" class=\"data row1 col1\" >1.8%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_9b521_level0_row2\" class=\"row_heading level0 row2\" >No Transit Use</th>\n",
" <td id=\"T_9b521_row2_col0\" class=\"data row2 col0\" >98.0%</td>\n",
" <td id=\"T_9b521_row2_col1\" class=\"data row2 col1\" >2.0%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_9b521_level0_row3\" class=\"row_heading level0 row3\" >All</th>\n",
" <td id=\"T_9b521_row3_col0\" class=\"data row3 col0\" >93.4%</td>\n",
" <td id=\"T_9b521_row3_col1\" class=\"data row3 col1\" >6.6%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x16a938850>"
]
},
"execution_count": 196,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hh_df_ct_r = pd.crosstab(\n",
" hh_df['transit_hh'], \n",
" hh_df['vehicle_hh'],\n",
" values = hh_df['WTHHFIN'], \n",
" aggfunc = sum,\n",
" normalize='index',\n",
" margins = True,\n",
" colnames=['HHold Vehicles'], \n",
" rownames=['HHold Transit Use in California (Source: NHTS 2017)'],\n",
")\n",
"s_hh_df_ct_r=hh_df_ct_r.style\\\n",
" .background_gradient()\\\n",
" .format(\"{:.1%}\")\n",
"\n",
"s_hh_df_ct_r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export Results"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [],
"source": [
"# Export to markdown\n",
"\n",
"try: \n",
" import tabulate\n",
"except:\n",
" !pip install tabulate\n",
" \n",
"with open(\"results.md\",\"w\") as f:\n",
" f.write(\"\\n\\n**Which transit-using households have vehicles?**\\n\\n\")\n",
" f.write(hh_df_ct.to_markdown(floatfmt=\".1%\"))\n",
" f.write(\"\\n\\n**Which vehicle-owning households ride transit?**\\n\\n\")\n",
" f.write(hh_df_ct_r.to_markdown(floatfmt=\".1%\"))\n",
" f.write(\"\\n\\n**Does it vary among households who are financially burdened by transportation?**\\n\\n\")\n",
" f.write(hh_df_3way_ct.to_markdown(floatfmt=\".1%\"))"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/elizabeth/opt/miniconda3/envs/calitp/lib/python3.10/site-packages/dataframe_image/_pandas_accessor.py:69: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n",
" html = '<div>' + obj.render() + '</div>'\n",
"[0324/132220.507694:INFO:headless_shell.cc(659)] Written to file /var/folders/60/xd2kny110pxfz3ln611jq7hm0000gn/T/tmp2anbgsyh/temp.png.\n",
"DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
"DEBUG:PIL.PngImagePlugin:STREAM b'iCCP' 41 295\n",
"DEBUG:PIL.PngImagePlugin:iCCP profile name b'Skia'\n",
"DEBUG:PIL.PngImagePlugin:Compression method 0\n",
"DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 348 8192\n",
"[0324/132222.411945:INFO:headless_shell.cc(659)] Written to file /var/folders/60/xd2kny110pxfz3ln611jq7hm0000gn/T/tmp3rt1v9vm/temp.png.\n",
"DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
"DEBUG:PIL.PngImagePlugin:STREAM b'iCCP' 41 295\n",
"DEBUG:PIL.PngImagePlugin:iCCP profile name b'Skia'\n",
"DEBUG:PIL.PngImagePlugin:Compression method 0\n",
"DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 348 8192\n",
"[0324/132225.100427:INFO:headless_shell.cc(659)] Written to file /var/folders/60/xd2kny110pxfz3ln611jq7hm0000gn/T/tmppj7306m7/temp.png.\n",
"DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
"DEBUG:PIL.PngImagePlugin:STREAM b'iCCP' 41 295\n",
"DEBUG:PIL.PngImagePlugin:iCCP profile name b'Skia'\n",
"DEBUG:PIL.PngImagePlugin:Compression method 0\n",
"DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 348 8192\n"
]
}
],
"source": [
"## Exporting to images\n",
"try:\n",
" import dataframe_image as dfi\n",
"except:\n",
" ! pip install dataframe_image\n",
" import dataframe_image as dfi\n",
"\n",
"dfi.export(s_hh_df_ct,\"hh_df_ct.png\")\n",
"dfi.export(s_hh_df_ct_r,\"hh_df_ct_r.png\")\n",
"dfi.export(s_hh_df_3way_ct,\"hh_df_3way_ct.png\")"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [],
"source": [
"# Plot Chord Chart\n",
"# Currently getting an error with this >:(\n",
"\n",
"try:\n",
" from chord import Chord\n",
"except:\n",
" ! pip install chord\n",
" from chord import Chord\n",
"\n",
"names=list(hh_df_ct.columns)\n",
"matrix = hh_df_ct.values.tolist()\n",
"ch=Chord(hh_df_ct,names)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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29efLi7vvXx16FwDAOHy6asJMb906dcfDR749efTNPVOjS1NLex9Yufn2u88PvQsAYBxidQIt7Tm4trTn4NrQOwAArpXXAAAAyBKrAABkTY9GvmoEAECTyyoAAFliFQCALLEKAECWWAUAIEusAgCQJVYBAMj6HVc/g0/PmESWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot Tree Map\n",
"# This is really ugly >:(\n",
"try:\n",
" import squarify \n",
"except:\n",
" ! pip install squarify \n",
" import squarify \n",
"\n",
"matrix = hh_df_ct.values.tolist()\n",
"values = [item for sublist in matrix for item in sublist]\n",
"\n",
"names=list(hh_df_ct.columns)\n",
"\n",
"fig, ax = plt.subplots(1, figsize = (12,12))\n",
"squarify.plot(sizes=values, \n",
" label=names, \n",
" alpha=.8 )\n",
"plt.axis('off')\n",
"plt.show()"
]
}
],
"metadata": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.2"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
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"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}