mirror of
https://github.com/DOI-DO/j40-cejst-2.git
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115 lines
3.4 KiB
Text
115 lines
3.4 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "20aa3891",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import csv\n",
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"import sys\n",
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"import os\n",
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"\n",
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"module_path = os.path.abspath(os.path.join(\"..\"))\n",
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"if module_path not in sys.path:\n",
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" sys.path.append(module_path)\n",
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"\n",
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"from etl.sources.census.etl_utils import get_state_fips_codes\n",
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"from utils import unzip_file_from_url, remove_all_from_dir\n",
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"\n",
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"DATA_PATH = Path.cwd().parent / \"data\"\n",
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"TMP_PATH = DATA_PATH / \"tmp\"\n",
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"HUD_RECAP_CSV_URL = \"https://opendata.arcgis.com/api/v3/datasets/56de4edea8264fe5a344da9811ef5d6e_0/downloads/data?format=csv&spatialRefId=4326\"\n",
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"CSV_PATH = DATA_PATH / \"dataset\" / \"hud_recap\"\n",
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"\n",
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"# Definining some variable names\n",
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"GEOID_TRACT_FIELD_NAME = \"GEOID10_TRACT\"\n",
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"HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME = \"hud_recap_priority_community\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b9455da5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data from https://hudgis-hud.opendata.arcgis.com/datasets/HUD::racially-or-ethnically-concentrated-areas-of-poverty-r-ecaps/about\n",
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"df = pd.read_csv(HUD_RECAP_CSV_URL, dtype={\"GEOID\": \"string\"})\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ca63e66c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Rename some fields\n",
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"df.rename(\n",
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" columns={\n",
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" \"GEOID\": GEOID_TRACT_FIELD_NAME,\n",
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" # Interestingly, there's no data dictionary for the RECAP data that I could find.\n",
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" # However, this site (http://www.schousing.com/library/Tax%20Credit/2020/QAP%20Instructions%20(2).pdf)\n",
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" # suggests:\n",
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" # \"If RCAP_Current for the tract in which the site is located is 1, the tract is an R/ECAP. If RCAP_Current is 0, it is not.\"\n",
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" \"RCAP_Current\": HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME,\n",
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" },\n",
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" inplace=True,\n",
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")\n",
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"\n",
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"# Convert to boolean\n",
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"df[HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME] = df[\n",
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" HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME\n",
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"].astype(\"bool\")\n",
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"\n",
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"df[HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME].value_counts()\n",
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"\n",
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"df.sort_values(by=GEOID_TRACT_FIELD_NAME, inplace=True)\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9fa2077a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# write csv\n",
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"CSV_PATH.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"# Drop unnecessary columns.\n",
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"df[[GEOID_TRACT_FIELD_NAME, HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME]].to_csv(\n",
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" CSV_PATH / \"usa.csv\", index=False\n",
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")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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