ncoda_07dv1_run_PREC_workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nDetVnGnoHR-"
   },
   "source": [
    "# Person Record File Workflow\n",
    "\n",
    "## Overview\n",
    "Functions to obtain and clean data required for the Person Record File in Python using Census API. \n",
    "\n",
    "The workflow produces a Person Record (PREC) file that can be linked to the Housing Unit Inventory. The file includes person level records with sex, age, race and ethnicity.\n",
    "\n",
    "Based on block level data from the \n",
    "2010 Census. \n",
    "\n",
    "The output of this workflow is a CSV file with the person record file.\n",
    "\n",
    "The output CSV is designed to be used in the Interdependent Networked Community Resilience Modeling Environment (IN-CORE) for the housing unit allocation model.\n",
    "\n",
    "IN-CORE is an open source python package that can be used to model the resilience of a community. To download IN-CORE, see:\n",
    "\n",
    "https://incore.ncsa.illinois.edu/\n",
    "\n",
    "\n",
    "## Instructions\n",
    "Users can run the workflow by executing each block of code in the notebook.\n",
    "\n",
    "Users can modify the code to select one county or multiple counties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of Program\n",
    "- program:    ncoda_07dv1_run_PREC_workflow\n",
    "- task:       Obtain and clean data for Person Record File.\n",
    "- See github commits for description of program updates\n",
    "- Current Version:    2022-12-02\n",
    "- project:    Interdependent Networked Community Resilience Modeling Environment (IN-CORE), Subtask 5.2 - Social Institutions\n",
    "- funding:\t  NIST Financial Assistance Award Numbers: 70NANB15H044 and 70NANB20H008 \n",
    "- author:     Nathanael Rosenheim\n",
    "\n",
    "- Suggested Citation:\n",
    "Rosenheim, Nathanael (2021) “Detailed Household and Housing Unit Characteristics: Data and Replication Code.” DesignSafe-CI. \n",
    "https://doi.org/10.17603/ds2-jwf6-s535."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Select the County or Counties\n",
    "The person record functions run for individual counties in the United States. \n",
    "A county is selected by entering the county FIPS code in the data dictionary below.\n",
    "Multiple counties can be selected by entering a list of county FIPS codes in the data dictionary.\n",
    "- For each county include the county name, this is used in the codebook.\n",
    "- For each community (1 county or a group of counties) include the community name, this is used in the codebook.\n",
    "\n",
    "\n",
    "### Example of data dictionary for one community with one county\n",
    "```\n",
    "communities = {'Lumberton_NC' : {\n",
    "                    'community_name' : 'Lumberton, NC',\n",
    "                    'counties' : { \n",
    "                        1 : {'FIPS Code' : '37155', 'Name' : 'Robeson County, NC'}}}}\n",
    "```\n",
    "\n",
    "### Example of data dictionary for one community with multiple counties\n",
    "```\n",
    "communities = {'Joplin_MO' : {\n",
    "                    'community_name' : 'Joplin, MO',\n",
    "                    'counties' : { \n",
    "                        1 : {'FIPS Code' : '29097', 'Name' : 'Jasper County, MO'},\n",
    "                        2 : {'FIPS Code' : '29145', 'Name' : 'Newton County, MO'}}}}\n",
    "```\n",
    "\n",
    "### How to locate your county FIPS code:\n",
    "- County FIPS codes can be found online at [USDA County FIPS codes](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/home/?cid=nrcs143_013697)\n",
    "- Google search \"FIPS code for county [county name]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Edit the data dictionary\n",
    "Modify the data dictionary below with your county FIPS Code, name and community name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example of data dictionary for one community with one county\n",
    "communities = {'Beaumont_TX' : {\n",
    "                    'community_name' : 'Beaumont, TX',\n",
    "                    'counties' : { \n",
    "                        1 : {'FIPS Code' : '48245', 'Name' : 'Jefferson County, TX'}}}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Run all of the code blocks in the notebook\n",
    "To run all of the code blocks in the notebook, find the \"Run All\" option:\n",
    "1. If there is a \"Run All\" button at the top of the notebook then click it.\n",
    "2. Else, in the \"Run\" menu, select \"Run All Cells\"\n",
    "\n",
    "After all of the code runs (approximately 2 minutes per 50,000 people), the output files (CSV and codebook) will be generated and saved in the folder \"OutputData\" in the directory where the notebook is saved.\n",
    "\n",
    "The notebook produces a log file which can be reviewed to see the full workflow process. The workflow depends on internet access to the Census API, which is a publicly available service.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup notebook environment to access Cloned Github Package\n",
    "This notebook uses functions that are in development. The current version of the package is available at:\n",
    "\n",
    "https://github.com/npr99/intersect-community-data\n",
    "\n",
    "Nathanael Rosenheim. (2022). npr99/intersect-community-data. Zenodo. https://doi.org/10.5281/zenodo.6476122\n",
    "\n",
    "A permanent copy of the package and example datasets are available in the DesignSafe-CI repository:\n",
    "\n",
    "Rosenheim, Nathanael (2021) “Detailed Household and Housing Unit Characteristics: Data and Replication Code.” DesignSafe-CI. \n",
    "https://doi.org/10.17603/ds2-jwf6-s535."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ub0JUKWHoHSE"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os # For saving output to path\n",
    "import sys\n",
    "import scooby # Reports Python environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------------------------------------\n",
      "  Date: Fri Dec 02 16:45:05 2022 Central Standard Time\n",
      "\n",
      "                OS : Windows\n",
      "            CPU(s) : 12\n",
      "           Machine : AMD64\n",
      "      Architecture : 64bit\n",
      "               RAM : 31.6 GiB\n",
      "       Environment : Jupyter\n",
      "\n",
      "  Python 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 05:37:49)\n",
      "  [MSC v.1916 64 bit (AMD64)]\n",
      "\n",
      "            pandas : 1.3.5\n",
      "             numpy : 1.21.6\n",
      "             scipy : 1.7.3\n",
      "           IPython : 7.32.0\n",
      "        matplotlib : 3.5.1\n",
      "            scooby : 0.5.12\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Generate report of Python environment\n",
    "print(scooby.Report(additional=['pandas']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To replicate this notebook Clone the Github Package to a folder that is a sibling of this notebook.\n",
    "# To access the sibling package you will need to append the parent directory ('..') to the system path list.\n",
    "# append the path of the directory that includes the github repository.\n",
    "# This step is not required when the package is in a folder below the notebook file.\n",
    "github_code_path  = \"\"\n",
    "sys.path.append(github_code_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\Users\\\\nathanael99\\\\MyProjects\\\\IN-CORE\\\\Tasks\\\\PublishHUIv2\\\\HousingUnitInventories_2022-03-03\\\\ReplicationCode\\\\intersect-community-data'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To reload submodules need to use this magic command to set autoreload on\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# open, read, and execute python program with reusable commands\n",
    "from pyncoda.ncoda_07e_generate_prec import generate_prec_functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating Person Record File v3.0.0 data for Beaumont, TX\n",
      "Jefferson County, TX : county FIPS Code 48245\n",
      "Creating folder OutputData/JeffersonCounty_TX to store output.\n",
      "Creating folder OutputData/JeffersonCounty_TX/00_logfiles\n",
      "     logfiles Folder purpose: Store text files with log of workflow.\n",
      "Creating folder OutputData/JeffersonCounty_TX/01_CommunitySourceData\n",
      "     CommunitySourceData Folder purpose: Source source data files. Helps with software development and replication.\n",
      "Creating folder OutputData/JeffersonCounty_TX/02_TidyCommunitySourceData\n",
      "     TidyCommunitySourceData Folder purpose: Cleaned source data and inputs for base inventory.\n",
      "Creating folder OutputData/JeffersonCounty_TX/03_BaseInventory\n",
      "     BaseInventory Folder purpose: Initial inventories before random merge. Use as inputs to uncertainty propagation.\n",
      "Creating folder OutputData/JeffersonCounty_TX/04_RandomMerge\n",
      "     RandomMerge Folder purpose: Results of random merge with flag columns.\n",
      "Creating folder OutputData/JeffersonCounty_TX/05_Verify\n",
      "     Verify Folder purpose: Results to compare inventories with source data.\n",
      "Creating folder OutputData/JeffersonCounty_TX/06_Explore\n",
      "     Explore Folder purpose: Explore output results.\n",
      "Creating folder OutputData/JeffersonCounty_TX/07_Uncertainty_propagation\n",
      "     Uncertainty_propagation Folder purpose: Results of MCS to determine which random seed to use for validation.\n",
      "Creating folder OutputData/JeffersonCounty_TX/08_Validation\n",
      "     Validation Folder purpose: Application using inventory e.g. IN-CORE.\n",
      "\n",
      "***************************************\n",
      "    Version control - list of installed packages\n",
      "***************************************\n",
      "\n",
      "# packages in environment at c:\\Users\\nathanael99\\Miniconda3\\envs\\pyhui20220422:\n",
      "#\n",
      "# Name                    Version                   Build  Channel\n",
      "argon2-cffi               21.3.0             pyhd8ed1ab_0    conda-forge\n",
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      "attrs                     21.4.0             pyhd8ed1ab_0    conda-forge\n",
      "backcall                  0.2.0              pyh9f0ad1d_0    conda-forge\n",
      "backports                 1.0                        py_2    conda-forge\n",
      "backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge\n",
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      "unicodedata2              14.0.0           py37hcc03f2d_1    conda-forge\n",
      "urllib3                   1.26.9             pyhd8ed1ab_0    conda-forge\n",
      "vc                        14.2                 hb210afc_6    conda-forge\n",
      "vs2015_runtime            14.29.30037          h902a5da_6    conda-forge\n",
      "wcwidth                   0.2.5              pyh9f0ad1d_2    conda-forge\n",
      "webencodings              0.5.1                      py_1    conda-forge\n",
      "wheel                     0.37.1             pyhd8ed1ab_0    conda-forge\n",
      "widgetsnbextension        3.6.0            py37h03978a9_0    conda-forge\n",
      "win_inet_pton             1.1.0            py37h03978a9_4    conda-forge\n",
      "winpty                    0.4.3                         4    conda-forge\n",
      "xorg-libxau               1.0.9                hcd874cb_0    conda-forge\n",
      "xorg-libxdmcp             1.1.3                hcd874cb_0    conda-forge\n",
      "xz                        5.2.5                h62dcd97_1    conda-forge\n",
      "zeromq                    4.3.4                h0e60522_1    conda-forge\n",
      "zipp                      3.8.0              pyhd8ed1ab_0    conda-forge\n",
      "zlib                      1.2.11            h8ffe710_1014    conda-forge\n",
      "zstd                      1.5.2                h6255e5f_0    conda-forge\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "***************************************\n",
      "    Obtain and clean core person record characteristics for Jefferson County, TX\n",
      "***************************************\n",
      "\n",
      "{'top': 'OutputData/JeffersonCounty_TX', 'logfiles': 'OutputData/JeffersonCounty_TX/00_logfiles', 'CommunitySourceData': 'OutputData/JeffersonCounty_TX/01_CommunitySourceData', 'TidyCommunitySourceData': 'OutputData/JeffersonCounty_TX/02_TidyCommunitySourceData', 'BaseInventory': 'OutputData/JeffersonCounty_TX/03_BaseInventory', 'RandomMerge': 'OutputData/JeffersonCounty_TX/04_RandomMerge', 'Verify': 'OutputData/JeffersonCounty_TX/05_Verify', 'Explore': 'OutputData/JeffersonCounty_TX/06_Explore', 'Uncertainty_propagation': 'OutputData/JeffersonCounty_TX/07_Uncertainty_propagation', 'Validation': 'OutputData/JeffersonCounty_TX/08_Validation'}\n",
      "{'top': 'OutputData/JeffersonCounty_TX', 'logfiles': 'OutputData/JeffersonCounty_TX/00_logfiles', 'CommunitySourceData': 'OutputData/JeffersonCounty_TX/01_CommunitySourceData', 'TidyCommunitySourceData': 'OutputData/JeffersonCounty_TX/02_TidyCommunitySourceData', 'BaseInventory': 'OutputData/JeffersonCounty_TX/03_BaseInventory', 'RandomMerge': 'OutputData/JeffersonCounty_TX/04_RandomMerge', 'Verify': 'OutputData/JeffersonCounty_TX/05_Verify', 'Explore': 'OutputData/JeffersonCounty_TX/06_Explore', 'Uncertainty_propagation': 'OutputData/JeffersonCounty_TX/07_Uncertainty_propagation', 'Validation': 'OutputData/JeffersonCounty_TX/08_Validation'}\n",
      "['sex', 'minageyrs', 'maxageyrs', 'byracehispan']\n",
      "\n",
      "**********************************\n",
      "Obtain data from Census API SEX BY AGE FOR SELECTED AGE CATEGORIES\n",
      "    Obtaining data for P012 SEX BY AGE FOR SELECTED AGE CATEGORIES by Total\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012003,P012004,P012005,P012006,P012007,P012008,P012009,P012010,P012011,P012012,P012013,P012014,P012015,P012016,P012017,P012018,P012019,P012020,P012021,P012022,P012023,P012024,P012025,P012027,P012028,P012029,P012030,P012031,P012032,P012033,P012034,P012035,P012036,P012037,P012038,P012039,P012040,P012041,P012042,P012043,P012044,P012045,P012046,P012047,P012048,P012049&in=state:48&in=county:245&for=block:*\n",
      "    Obtaining data for P012A SEX BY AGE FOR SELECTED AGE CATEGORIES by White alone\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012A003,P012A004,P012A005,P012A006,P012A007,P012A008,P012A009,P012A010,P012A011,P012A012,P012A013,P012A014,P012A015,P012A016,P012A017,P012A018,P012A019,P012A020,P012A021,P012A022,P012A023,P012A024,P012A025,P012A027,P012A028,P012A029,P012A030,P012A031,P012A032,P012A033,P012A034,P012A035,P012A036,P012A037,P012A038,P012A039,P012A040,P012A041,P012A042,P012A043,P012A044,P012A045,P012A046,P012A047,P012A048,P012A049&in=state:48&in=county:245&for=block:*\n",
      "    Obtaining data for P012B SEX BY AGE FOR SELECTED AGE CATEGORIES by Black or African American alone\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012B003,P012B004,P012B005,P012B006,P012B007,P012B008,P012B009,P012B010,P012B011,P012B012,P012B013,P012B014,P012B015,P012B016,P012B017,P012B018,P012B019,P012B020,P012B021,P012B022,P012B023,P012B024,P012B025,P012B027,P012B028,P012B029,P012B030,P012B031,P012B032,P012B033,P012B034,P012B035,P012B036,P012B037,P012B038,P012B039,P012B040,P012B041,P012B042,P012B043,P012B044,P012B045,P012B046,P012B047,P012B048,P012B049&in=state:48&in=county:245&for=block:*\n",
      "    Obtaining data for P012C SEX BY AGE FOR SELECTED AGE CATEGORIES by American Indian and Alaska Native alone\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012C003,P012C004,P012C005,P012C006,P012C007,P012C008,P012C009,P012C010,P012C011,P012C012,P012C013,P012C014,P012C015,P012C016,P012C017,P012C018,P012C019,P012C020,P012C021,P012C022,P012C023,P012C024,P012C025,P012C027,P012C028,P012C029,P012C030,P012C031,P012C032,P012C033,P012C034,P012C035,P012C036,P012C037,P012C038,P012C039,P012C040,P012C041,P012C042,P012C043,P012C044,P012C045,P012C046,P012C047,P012C048,P012C049&in=state:48&in=county:245&for=block:*\n",
      "    Obtaining data for P012D SEX BY AGE FOR SELECTED AGE CATEGORIES by Asian alone\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012D003,P012D004,P012D005,P012D006,P012D007,P012D008,P012D009,P012D010,P012D011,P012D012,P012D013,P012D014,P012D015,P012D016,P012D017,P012D018,P012D019,P012D020,P012D021,P012D022,P012D023,P012D024,P012D025,P012D027,P012D028,P012D029,P012D030,P012D031,P012D032,P012D033,P012D034,P012D035,P012D036,P012D037,P012D038,P012D039,P012D040,P012D041,P012D042,P012D043,P012D044,P012D045,P012D046,P012D047,P012D048,P012D049&in=state:48&in=county:245&for=block:*\n",
      "    Obtaining data for P012E SEX BY AGE FOR SELECTED AGE CATEGORIES by Native Hawaiian and Other Pacific Islander alone\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012E003,P012E004,P012E005,P012E006,P012E007,P012E008,P012E009,P012E010,P012E011,P012E012,P012E013,P012E014,P012E015,P012E016,P012E017,P012E018,P012E019,P012E020,P012E021,P012E022,P012E023,P012E024,P012E025,P012E027,P012E028,P012E029,P012E030,P012E031,P012E032,P012E033,P012E034,P012E035,P012E036,P012E037,P012E038,P012E039,P012E040,P012E041,P012E042,P012E043,P012E044,P012E045,P012E046,P012E047,P012E048,P012E049&in=state:48&in=county:245&for=block:*\n",
      "    Obtaining data for P012F SEX BY AGE FOR SELECTED AGE CATEGORIES by Some Other Race alone\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012F003,P012F004,P012F005,P012F006,P012F007,P012F008,P012F009,P012F010,P012F011,P012F012,P012F013,P012F014,P012F015,P012F016,P012F017,P012F018,P012F019,P012F020,P012F021,P012F022,P012F023,P012F024,P012F025,P012F027,P012F028,P012F029,P012F030,P012F031,P012F032,P012F033,P012F034,P012F035,P012F036,P012F037,P012F038,P012F039,P012F040,P012F041,P012F042,P012F043,P012F044,P012F045,P012F046,P012F047,P012F048,P012F049&in=state:48&in=county:245&for=block:*\n",
      "    Obtaining data for P012G SEX BY AGE FOR SELECTED AGE CATEGORIES by Two or More Races\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012G003,P012G004,P012G005,P012G006,P012G007,P012G008,P012G009,P012G010,P012G011,P012G012,P012G013,P012G014,P012G015,P012G016,P012G017,P012G018,P012G019,P012G020,P012G021,P012G022,P012G023,P012G024,P012G025,P012G027,P012G028,P012G029,P012G030,P012G031,P012G032,P012G033,P012G034,P012G035,P012G036,P012G037,P012G038,P012G039,P012G040,P012G041,P012G042,P012G043,P012G044,P012G045,P012G046,P012G047,P012G048,P012G049&in=state:48&in=county:245&for=block:*\n",
      "    Obtaining data for P012I SEX BY AGE FOR SELECTED AGE CATEGORIES by White alone, not Hispanic\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012I003,P012I004,P012I005,P012I006,P012I007,P012I008,P012I009,P012I010,P012I011,P012I012,P012I013,P012I014,P012I015,P012I016,P012I017,P012I018,P012I019,P012I020,P012I021,P012I022,P012I023,P012I024,P012I025,P012I027,P012I028,P012I029,P012I030,P012I031,P012I032,P012I033,P012I034,P012I035,P012I036,P012I037,P012I038,P012I039,P012I040,P012I041,P012I042,P012I043,P012I044,P012I045,P012I046,P012I047,P012I048,P012I049&in=state:48&in=county:245&for=block:*\n",
      "    Obtaining data for P012H SEX BY AGE FOR SELECTED AGE CATEGORIES by Hispanic or Latino\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P012H003,P012H004,P012H005,P012H006,P012H007,P012H008,P012H009,P012H010,P012H011,P012H012,P012H013,P012H014,P012H015,P012H016,P012H017,P012H018,P012H019,P012H020,P012H021,P012H022,P012H023,P012H024,P012H025,P012H027,P012H028,P012H029,P012H030,P012H031,P012H032,P012H033,P012H034,P012H035,P012H036,P012H037,P012H038,P012H039,P012H040,P012H041,P012H042,P012H043,P012H044,P012H045,P012H046,P012H047,P012H048,P012H049&in=state:48&in=county:245&for=block:*\n",
      "\n",
      "**********************************\n",
      "Create mutually exclusive dataframe for:\n",
      "      White alone, Not Hispanic\n",
      "      White alone, Hispanic\n",
      "      Black or African American alone\n",
      "      American Indian and Alaska Native alone\n",
      "      Asian alone\n",
      "      Native Hawaiian and Other Pacific Islander alone\n",
      "      Some Other Race alone\n",
      "      Two or More Races\n",
      "\n",
      "**********************************\n",
      "Reshape dataframe to convert unit of analysis\n",
      "   For mutually exclusive dataframe for: White alone, Not Hispanic\n",
      "       Unit of analysis converted from 10760 block s, to 54139 person\n",
      "   For mutually exclusive dataframe for: White alone, Hispanic\n",
      "       Unit of analysis converted from 10760 block s, to 13880 person\n",
      "   For mutually exclusive dataframe for: Black or African American alone\n",
      "       Unit of analysis converted from 10760 block s, to 42886 person\n",
      "   For mutually exclusive dataframe for: American Indian and Alaska Native alone\n",
      "       Unit of analysis converted from 10760 block s, to 1191 person\n",
      "   For mutually exclusive dataframe for: Asian alone\n",
      "       Unit of analysis converted from 10760 block s, to 6033 person\n",
      "   For mutually exclusive dataframe for: Native Hawaiian and Other Pacific Islander alone\n",
      "       Unit of analysis converted from 10760 block s, to 74 person\n",
      "   For mutually exclusive dataframe for: Some Other Race alone\n",
      "       Unit of analysis converted from 10760 block s, to 13722 person\n",
      "   For mutually exclusive dataframe for: Two or More Races\n",
      "       Unit of analysis converted from 10760 block s, to 4129 person\n",
      "Base Housing Unit Inventory has new characteristic hispan\n",
      "Graft process will predict missing values of  hispan\n",
      "\n",
      "***************************************\n",
      "    Base Inventory has 120699 observations not set\n",
      "***************************************\n",
      "\n",
      "\n",
      "***************************************\n",
      "    Predicting hispan based on ['sex', 'minageyrs', 'maxageyrs', 'hispanbyP12HAI', 'byracehispan'] ['sex', 'minageyrs', 'maxageyrs'] P12HAI\n",
      "***************************************\n",
      "\n",
      "\n",
      "***************************************\n",
      "    Base Inventory has 120699 observations not set\n",
      "***************************************\n",
      "\n",
      "['sex', 'minageyrs', 'maxageyrs', 'hispanbyP12HAI', 'byracehispan']\n",
      "\n",
      "**********************************\n",
      "Obtain data from Census API SEX BY AGE FOR SELECTED AGE CATEGORIES\n",
      "    Obtaining data for P012A SEX BY AGE FOR SELECTED AGE CATEGORIES by White alone\n",
      "Dictionary file OutputData/JeffersonCounty_TX/01_CommunitySourceData/api_census_gov/P012A_48245_2010.json Already exists - Skipping API Call.\n",
      "    Obtaining data for P012I SEX BY AGE FOR SELECTED AGE CATEGORIES by White alone, not Hispanic\n",
      "Dictionary file OutputData/JeffersonCounty_TX/01_CommunitySourceData/api_census_gov/P012I_48245_2010.json Already exists - Skipping API Call.\n",
      "    Obtaining data for P012H SEX BY AGE FOR SELECTED AGE CATEGORIES by Hispanic or Latino\n",
      "Dictionary file OutputData/JeffersonCounty_TX/01_CommunitySourceData/api_census_gov/P012H_48245_2010.json Already exists - Skipping API Call.\n",
      "\n",
      "**********************************\n",
      "Create mutually exclusive dataframe for:\n",
      "      Hispanic or Latino, any race Not white\n",
      "\n",
      "**********************************\n",
      "Reshape dataframe to convert unit of analysis\n",
      "   For mutually exclusive dataframe for: Hispanic or Latino, any race Not white\n",
      "       Unit of analysis converted from 10760 block s, to 15929 person\n",
      "hispanbyP12HAI\n",
      "Fill missing values with 0\n",
      "\n",
      "***************************************\n",
      "    Predicting hispan based on ['race', 'hispanbyP5'] ['race'] P5\n",
      "***************************************\n",
      "\n",
      "\n",
      "***************************************\n",
      "    Base Inventory has 37524 observations not set\n",
      "***************************************\n",
      "\n",
      "['race', 'hispanbyP5']\n",
      "\n",
      "**********************************\n",
      "Obtain data from Census API HISPANIC OR LATINO ORIGIN BY RACE\n",
      "    Obtaining data for P005 HISPANIC OR LATINO ORIGIN BY RACE by Race and Hispanic Not Applicable\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,P005011,P005012,P005013,P005014,P005015,P005016,P005017&in=state:48&in=county:245&for=block:*\n",
      "\n",
      "**********************************\n",
      "Reshape dataframe to convert unit of analysis\n",
      "   For mutually exclusive dataframe for: HISPANIC OR LATINO ORIGIN BY RACE\n",
      "       Unit of analysis converted from 10760 block s, to 5928 person\n",
      "hispanbyP5\n",
      "Fill missing values with 0\n",
      "['Block2010str', 'sex', 'minageyrs', 'maxageyrs']\n",
      "Updating hispanbyP12HAI_counter  based on total probability and ['Block2010str', 'sex', 'minageyrs', 'maxageyrs']\n",
      "['Block2010str', 'race']\n",
      "Updating hispanbyP5_counter  based on total probability and ['Block2010str', 'race']\n",
      "Merge vars for hispanbyP12HAI_counter = ['Block2010str', 'sex', 'minageyrs', 'maxageyrs']\n",
      "Updating flags based on preccount_hispanbyP12HAIupdated\n",
      "Merge vars for hispanbyP5_counter = ['Block2010str', 'race']\n",
      "Updating flags based on preccount_hispanbyP5updated\n",
      "Length of Set1 split dataframe 131574\n",
      "Length of Not Set split dataframe 13114\n",
      "Length of hispanbyP12HAI split dataframe 83175\n",
      "Length of hispanbyP5 split dataframe 20925\n",
      "Length of hispanbyP12HAI_counter split dataframe 3357\n",
      "Length of hispanbyP5_counter split dataframe 128\n",
      "\n",
      "\n",
      "Shape of dataframe after total sum: (252273, 24)\n",
      "['prob_hispanbyP12HAI', 'prob_hispanbyP5']\n",
      "hispanbyP12HAI\n",
      "hispanbyP5\n",
      "Dictionary file OutputData/00_datastructures/api_census_gov/PCT12_2010_datastructurev0-2-0.txt Already exists - Skipping API Call.\n",
      "{'top': 'OutputData/JeffersonCounty_TX', 'logfiles': 'OutputData/JeffersonCounty_TX/00_logfiles', 'CommunitySourceData': 'OutputData/JeffersonCounty_TX/01_CommunitySourceData', 'TidyCommunitySourceData': 'OutputData/JeffersonCounty_TX/02_TidyCommunitySourceData', 'BaseInventory': 'OutputData/JeffersonCounty_TX/03_BaseInventory', 'RandomMerge': 'OutputData/JeffersonCounty_TX/04_RandomMerge', 'Verify': 'OutputData/JeffersonCounty_TX/05_Verify', 'Explore': 'OutputData/JeffersonCounty_TX/06_Explore', 'Uncertainty_propagation': 'OutputData/JeffersonCounty_TX/07_Uncertainty_propagation', 'Validation': 'OutputData/JeffersonCounty_TX/08_Validation'}\n",
      "['sex', 'minageyrs', 'maxageyrs']\n",
      "\n",
      "**********************************\n",
      "Obtain data from Census API SEX BY AGE\n",
      "    Obtaining data for PCT012 SEX BY AGE by Race and Hispanic Not Applicable\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,PCT012003,PCT012004,PCT012005,PCT012006,PCT012007,PCT012008,PCT012009,PCT012010,PCT012011,PCT012012,PCT012013,PCT012014,PCT012015,PCT012016,PCT012017,PCT012018,PCT012019,PCT012020,PCT012021,PCT012022,PCT012023,PCT012024,PCT012025,PCT012026,PCT012027,PCT012028,PCT012029,PCT012030,PCT012031,PCT012032,PCT012033,PCT012034,PCT012035,PCT012036,PCT012037,PCT012038,PCT012039,PCT012040,PCT012041,PCT012042,PCT012043,PCT012044,PCT012045,PCT012046&in=state:48&in=county:245&for=tract:*\n",
      "\n",
      "**********************************\n",
      "Reshape dataframe to convert unit of analysis\n",
      "   For mutually exclusive dataframe for: SEX BY AGE\n",
      "       Unit of analysis converted from 73 tract s, to 3077 person\n",
      "['sex', 'minageyrs', 'maxageyrs']\n",
      "\n",
      "**********************************\n",
      "Obtain data from Census API SEX BY AGE\n",
      "    Obtaining data for PCT012 SEX BY AGE by Race and Hispanic Not Applicable\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,PCT012047,PCT012048,PCT012049,PCT012050,PCT012051,PCT012052,PCT012053,PCT012054,PCT012055,PCT012056,PCT012057,PCT012058,PCT012059,PCT012060,PCT012061,PCT012062,PCT012063,PCT012064,PCT012065,PCT012066,PCT012067,PCT012068,PCT012069,PCT012070,PCT012071,PCT012072,PCT012073,PCT012074,PCT012075,PCT012076,PCT012077,PCT012078,PCT012079,PCT012080,PCT012081,PCT012082,PCT012083,PCT012084,PCT012085,PCT012086,PCT012087,PCT012088,PCT012089,PCT012090&in=state:48&in=county:245&for=tract:*\n",
      "\n",
      "**********************************\n",
      "Reshape dataframe to convert unit of analysis\n",
      "   For mutually exclusive dataframe for: SEX BY AGE\n",
      "       Unit of analysis converted from 73 tract s, to 3024 person\n",
      "['sex', 'minageyrs', 'maxageyrs']\n",
      "\n",
      "**********************************\n",
      "Obtain data from Census API SEX BY AGE\n",
      "    Obtaining data for PCT012 SEX BY AGE by Race and Hispanic Not Applicable\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,PCT012091,PCT012092,PCT012093,PCT012094,PCT012095,PCT012096,PCT012097,PCT012098,PCT012099,PCT012100,PCT012101,PCT012102,PCT012103,PCT012104,PCT012105,PCT012107,PCT012108,PCT012109,PCT012110,PCT012111,PCT012112,PCT012113,PCT012114,PCT012115,PCT012116,PCT012117,PCT012118,PCT012119,PCT012120,PCT012121,PCT012122,PCT012123,PCT012124,PCT012125,PCT012126,PCT012127,PCT012128,PCT012129,PCT012130,PCT012131,PCT012132,PCT012133,PCT012134,PCT012135&in=state:48&in=county:245&for=tract:*\n",
      "\n",
      "**********************************\n",
      "Reshape dataframe to convert unit of analysis\n",
      "   For mutually exclusive dataframe for: SEX BY AGE\n",
      "       Unit of analysis converted from 73 tract s, to 2342 person\n",
      "['sex', 'minageyrs', 'maxageyrs']\n",
      "\n",
      "**********************************\n",
      "Obtain data from Census API SEX BY AGE\n",
      "    Obtaining data for PCT012 SEX BY AGE by Race and Hispanic Not Applicable\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,PCT012136,PCT012137,PCT012138,PCT012139,PCT012140,PCT012141,PCT012142,PCT012143,PCT012144,PCT012145,PCT012146,PCT012147,PCT012148,PCT012149,PCT012150,PCT012151,PCT012152,PCT012153,PCT012154,PCT012155,PCT012156,PCT012157,PCT012158,PCT012159,PCT012160,PCT012161,PCT012162,PCT012163,PCT012164,PCT012165,PCT012166,PCT012167,PCT012168,PCT012169,PCT012170,PCT012171,PCT012172,PCT012173,PCT012174,PCT012175,PCT012176,PCT012177,PCT012178,PCT012179&in=state:48&in=county:245&for=tract:*\n",
      "\n",
      "**********************************\n",
      "Reshape dataframe to convert unit of analysis\n",
      "   For mutually exclusive dataframe for: SEX BY AGE\n",
      "       Unit of analysis converted from 73 tract s, to 3023 person\n",
      "['sex', 'minageyrs', 'maxageyrs']\n",
      "\n",
      "**********************************\n",
      "Obtain data from Census API SEX BY AGE\n",
      "    Obtaining data for PCT012 SEX BY AGE by Race and Hispanic Not Applicable\n",
      "       Census API data from: https://api.census.gov/data/2010/dec/sf1?get=GEO_ID,PCT012180,PCT012181,PCT012182,PCT012183,PCT012184,PCT012185,PCT012186,PCT012187,PCT012188,PCT012189,PCT012190,PCT012191,PCT012192,PCT012193,PCT012194,PCT012195,PCT012196,PCT012197,PCT012198,PCT012199,PCT012200,PCT012201,PCT012202,PCT012203,PCT012204,PCT012205,PCT012206,PCT012207,PCT012208,PCT012209&in=state:48&in=county:245&for=tract:*\n",
      "\n",
      "**********************************\n",
      "Reshape dataframe to convert unit of analysis\n",
      "   For mutually exclusive dataframe for: SEX BY AGE\n",
      "       Unit of analysis converted from 73 tract s, to 1516 person\n",
      "\n",
      "***************************************\n",
      "    Person Block by Age with Tract by Age data.\n",
      "***************************************\n",
      "\n",
      "Round 1\n",
      "\n",
      "***************************************\n",
      "***************************************\n",
      "\n",
      "Performing random merge at geography level: Tract\n",
      "\n",
      "***************************************\n",
      "***************************************\n",
      "\n",
      "\n",
      "***************************************\n",
      "***************************************\n",
      "\n",
      "By original common group vars and by groups variables.\n",
      "\n",
      "***************************************\n",
      "***************************************\n",
      "\n",
      "Running random merge by ['Tract2010', 'sex', 'agegroupP12']\n",
      "\n",
      "***************************************\n",
      "    Setting up  primary data with primary key and flags\n",
      "***************************************\n",
      "\n",
      "Longest Block2010 : 482459800001017\n",
      "Block2010 Expected Length 15 Available Length 15\n",
      "Geolevels available []\n",
      "Geolvarids available ['Block2010']\n",
      "Adding Tract2010 expected length 11\n",
      "Dataframe has Block 2010 for new geovar Tract2010\n",
      "Confirming Tract2010 has expected length.\n",
      "Longest Tract2010 : 48245980000\n",
      "Tract2010 Expected Length 11 Available Length 11\n",
      "Checking primary key name precid\n",
      "Primary key variable precid is unique.\n",
      "Primary key precid has no missing values\n",
      "Initializing primary flag set variable for randagePCT12\n",
      "New flag: randagePCT12_flagsetrm\n",
      "['precid', 'Tract2010', 'Block2010', 'Block2010str', 'sex', 'minageyrs', 'maxageyrs', 'race', 'hispan', 'prec_counter', 'hispan_flag', 'hispan_flagset', 'totalprob_hispan', 'hispanbyP12HAI', 'preccount_hispanbyP12HAI', 'sumby_hispanbyP12HAI', 'prob_hispanbyP12HAI', 'hispanbyP5', 'preccount_hispanbyP5', 'sumby_hispanbyP5', 'prob_hispanbyP5', 'hispanbyP12HAI_counter', 'hispanbyP5_counter', 'preccount_hispanbyP12HAIupdated', 'preccount_hispanbyP5updated', 'randageP12', 'agegroupP12']\n",
      "Initializing geovar flag set variable for randagePCT12 at geolevel Tract2010\n",
      "New flag: randagePCT12_Tract2010_flagsetrm\n",
      "Observations without primary flag set 252273\n",
      "Observations without geovar flag set 252273\n",
      "After updated observations without geovar flag set 252273\n",
      "Setting 252273 flags for data without randagePCT12 before merge.\n",
      "0 observations do not have required variable Tract2010\n",
      "0 observations do not have required variable sex\n",
      "0 observations do not have required variable agegroupP12\n",
      "0 observations do not have required variable sex\n",
      "Setting 0 flags for observations without required variable Tract2010\n",
      "Setting 0 flags for observations without required variable sex\n",
      "Setting 0 flags for observations without required variable agegroupP12\n",
      "Attempting random merge for 252273 primary observations.\n",
      "\n",
      "***************************************\n",
      "    Preparing primary by All data for random merge.\n",
      "***************************************\n",
      "\n",
      "Sorting by  ['precid']\n",
      "Sorting before random merge order by ['Tract2010', 'sex', 'agegroupP12', 'random_order']\n",
      "Generating random merge order by ['Tract2010', 'sex', 'agegroupP12']\n",
      "\n",
      "***************************************\n",
      "    Setting up  secondary data with primary key and flags\n",
      "***************************************\n",
      "\n",
      "Check length of state expected length 2\n",
      "Longest state : 48\n",
      "state Expected Length 2 Available Length 2\n",
      "state is type object\n",
      "after update state is type object\n",
      "Check length of county expected length 3\n",
      "Longest county : 245\n",
      "county Expected Length 3 Available Length 3\n",
      "county is type object\n",
      "after update county is type object\n",
      "Check length of tract expected length 6\n",
      "Longest tract : 980000\n",
      "tract Expected Length 6 Available Length 6\n",
      "tract is type object\n",
      "after update tract is type object\n",
      "Geolevels available ['state', 'county', 'tract']\n",
      "Geolvarids available []\n",
      "Adding Tract2010 expected length 11\n",
      "Dataframe has required geo levels ['state', 'county', 'tract']\n",
      "state is type object\n",
      "county is type object\n",
      "tract is type object\n",
      "Confirming Tract2010 has expected length.\n",
      "Longest Tract2010 : 48245980000\n",
      "Tract2010 Expected Length 11 Available Length 11\n",
      "Checking primary key name uniqueidPCT12\n",
      "Primary key variable uniqueidPCT12 is unique.\n",
      "Primary key uniqueidPCT12 has no missing values\n",
      "Initializing primary flag set variable for randagePCT12\n",
      "New flag: randagePCT12_flagsetrm\n",
      "['uniqueidPCT12', 'Tract2010', 'index', 'GEO_ID', 'state', 'county', 'tract', 'sex', 'minageyrs', 'maxageyrs', 'prec_counter', 'randagePCT12', 'agegroupP12']\n",
      "Initializing geovar flag set variable for randagePCT12 at geolevel Tract2010\n",
      "New flag: randagePCT12_Tract2010_flagsetrm\n",
      "Observations without primary flag set 252273\n",
      "Observations without geovar flag set 252273\n",
      "After updated observations without geovar flag set 252273\n",
      "Setting 252273 flags for randagePCT12 secondary data not used.\n",
      "0 observations do not have required variable Tract2010\n",
      "0 observations do not have required variable sex\n",
      "0 observations do not have required variable agegroupP12\n",
      "0 observations do not have required variable sex\n",
      "Setting 0 flags for observations without required variable Tract2010\n",
      "Setting 0 flags for observations without required variable sex\n",
      "Setting 0 flags for observations without required variable agegroupP12\n",
      "Attempting random merge for 252273 secondary observations.\n",
      "\n",
      "***************************************\n",
      "    Preparing secondary by All data for random merge.\n",
      "***************************************\n",
      "\n",
      "Sorting by  ['uniqueidPCT12']\n",
      "Sorting before random merge order by ['Tract2010', 'sex', 'agegroupP12', 'random_order']\n",
      "Generating random merge order by ['Tract2010', 'sex', 'agegroupP12']\n",
      "All\n",
      "\n",
      "***************************************\n",
      "    Random Merge primary with secondary by All\n",
      "***************************************\n",
      "\n",
      "Check merge vars includes geovarid: ['Tract2010', 'sex', 'agegroupP12', 'random_mergeorder']\n",
      "Primary data frame has extra randagePCT12  observations with no match: 0\n",
      "Observations with no match filled with -999\n",
      "Merge found extra randagePCT12  observations: 0\n",
      "\n",
      "***************************************\n",
      "    Set Flags after Merge\n",
      "***************************************\n",
      "\n",
      "['precid', 'Tract2010', 'sex', 'agegroupP12', 'random_mergeorder', 'uniqueidPCT12', 'randagePCT12', 'check_merge', 'randagePCT12_flagsetrm', 'randagePCT12_Tract2010_flagsetrm']\n",
      "Observations without primary flag set 252273\n",
      "Observations without geovar flag set 252273\n",
      "After updated observations without geovar flag set 252273\n",
      "Round = 1\n",
      "Setting 252273 flags for observations set by random merge using both primary and secondary data.\n",
      "Setting 0 flags for observations set by random merge using only primary data.\n",
      "0 observations do not have required variable Tract2010\n",
      "0 observations do not have required variable sex\n",
      "0 observations do not have required variable agegroupP12\n",
      "0 observations do not have required variable sex\n",
      "Setting 0 flags for observations without required variable Tract2010\n",
      "Setting 0 flags for observations without required variable sex\n",
      "Setting 0 flags for observations without required variable agegroupP12\n",
      "Observations geovar flag set 1 = 252273\n",
      "After Recombine flag set 1 = 252273\n",
      "\n",
      "***************************************\n",
      "   Generate output data\n",
      "***************************************\n",
      "\n",
      "Flag vars available ['randagePCT12_flagsetrm', 'randagePCT12_Tract2010_flagsetrm']\n",
      "Before update output_df observations flag set 1 = 0\n",
      "Before update results observations flag set 1 = 252273\n",
      "After update observations geovar flag set 1 = 252273\n",
      "\n",
      "***************************************\n",
      "    Check random merge results for primary data.\n",
      "***************************************\n",
      "\n",
      "Check by geovar flag randagePCT12_Tract2010_flagsetrm\n",
      "Observations flag not equal to 0 252273\n",
      "Input and output data have the same length 252273\n",
      "Outputdata has 252273 Observations with predicted randagePCT12\n",
      "Percent left to predict:  0.00\n",
      "\n",
      "***************************************\n",
      "    Overwrite input data with update output data.\n",
      "***************************************\n",
      "\n",
      "Flag vars available ['randagePCT12_flagsetrm', 'randagePCT12_Tract2010_flagsetrm']\n",
      "Before update output_df observations flag set 1 = 0\n",
      "Before update results observations flag set 1 = 252273\n",
      "After update observations geovar flag set 1 = 252273\n",
      "\n",
      "***************************************\n",
      "    Check random merge results for secondary data.\n",
      "***************************************\n",
      "\n",
      "Check by geovar flag randagePCT12_Tract2010_flagsetrm\n",
      "Observations flag not equal to 0 252273\n",
      "Input and output data have the same length 252273\n",
      "Outputdata has 252273 Observations with predicted randagePCT12\n",
      "Percent left to predict:  0.00\n",
      "\n",
      "***************************************\n",
      "    Overwrite input data with update output data.\n",
      "***************************************\n",
      "\n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++\n",
      "Percent left to predict:   0.000000\n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      "***************************************\n",
      "    Random merge complete.\n",
      "***************************************\n",
      "\n",
      "Save primary and secondary files with all columns\n",
      "\n",
      "***************************************\n",
      "    Try to polish final hui data.\n",
      "***************************************\n",
      "\n",
      "\n",
      "***************************************\n",
      "    Try to polish final prec data.\n",
      "***************************************\n",
      "\n",
      "Drop extra columns.\n",
      "\n",
      "***************************************\n",
      "    Save cleaned data file.\n",
      "***************************************\n",
      "\n",
      "File saved: c:\\Users\\nathanael99\\MyProjects\\IN-CORE\\Tasks\\PublishHUIv2\\HousingUnitInventories_2022-03-03\\ReplicationCode\\intersect-community-data/OutputData/JeffersonCounty_TX/prec_v3-0-0_48245_2010_rs1000.csv\n",
      "\n",
      "***************************************\n",
      "    Save cleaned data file.\n",
      "***************************************\n",
      "\n",
      "File saved: c:\\Users\\nathanael99\\MyProjects\\IN-CORE\\Tasks\\PublishHUIv2\\HousingUnitInventories_2022-03-03\\ReplicationCode\\intersect-community-data/OutputData/JeffersonCounty_TX/prec_v3-0-0_48245_2010_rs1000.csv\n"
     ]
    }
   ],
   "source": [
    "version = '3.0.0'\n",
    "version_text = 'v3-0-0'\n",
    "\n",
    "# Save Outputfolder - due to long folder name paths output saved to folder with shorter name\n",
    "# files from this program will be saved with the program name - \n",
    "# this helps to follow the overall workflow\n",
    "outputfolder = \"OutputData\"\n",
    "# Make directory to save output\n",
    "if not os.path.exists(outputfolder):\n",
    "    os.mkdir(outputfolder)\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "seed = 1000\n",
    "basevintage = 2010\n",
    "\n",
    "generate_prec_df = generate_prec_functions(\n",
    "                    communities =   communities,\n",
    "                    seed =          seed,\n",
    "                    version =       version,\n",
    "                    version_text=   version_text,\n",
    "                    basevintage=    basevintage,\n",
    "                    outputfolder=   outputfolder\n",
    "                    )\n",
    "\n",
    "prec_df = generate_prec_df.generate_prec_v300()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precid</th>\n",
       "      <th>Tract2010</th>\n",
       "      <th>Block2010</th>\n",
       "      <th>Block2010str</th>\n",
       "      <th>sex</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  precid    Tract2010        Block2010      Block2010str  sex  \\\n",
       "0  B482450001011000P0001  48245000101  482450001011000  B482450001011000    1   \n",
       "1  B482450001011000P0002  48245000101  482450001011000  B482450001011000    1   \n",
       "2  B482450001011000P0003  48245000101  482450001011000  B482450001011000    1   \n",
       "3  B482450001011000P0004  48245000101  482450001011000  B482450001011000    1   \n",
       "4  B482450001011000P0005  48245000101  482450001011000  B482450001011000    1   \n",
       "\n",
       "   race  hispan  randageP12  agegroupP12  randagePCT12  \n",
       "0     1       0           3            1             4  \n",
       "1     1       0           3            1             2  \n",
       "2     1       0           3            1             4  \n",
       "3     1       0           0            1             3  \n",
       "4     1       0           3            1             1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th>103</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  precid  Tract2010  Block2010  Block2010str  race  hispan  \\\n",
       "sex randagePCT12                                                             \n",
       "1   0               1812       1812       1812          1812  1812    1812   \n",
       "    1               1724       1724       1724          1724  1724    1724   \n",
       "    2               1696       1696       1696          1696  1696    1696   \n",
       "    3               1819       1819       1819          1819  1819    1819   \n",
       "    4               1646       1646       1646          1646  1646    1646   \n",
       "...                  ...        ...        ...           ...   ...     ...   \n",
       "2   103                2          2          2             2     2       2   \n",
       "    105                1          1          1             1     1       1   \n",
       "    106                1          1          1             1     1       1   \n",
       "    107                1          1          1             1     1       1   \n",
       "    108                1          1          1             1     1       1   \n",
       "\n",
       "                  randageP12  agegroupP12  \n",
       "sex randagePCT12                           \n",
       "1   0                   1812         1812  \n",
       "    1                   1724         1724  \n",
       "    2                   1696         1696  \n",
       "    3                   1819         1819  \n",
       "    4                   1646         1646  \n",
       "...                      ...          ...  \n",
       "2   103                    2            2  \n",
       "    105                    1            1  \n",
       "    106                    1            1  \n",
       "    107                    1            1  \n",
       "    108                    1            1  \n",
       "\n",
       "[210 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "prec_df.groupby(['sex','randagePCT12']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyncoda.ncoda_04c_poptableresults import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beaumont, TX\n"
     ]
    }
   ],
   "source": [
    "for community in communities.keys():\n",
    "    community_name = communities[community]['community_name']\n",
    "print(community_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7c6d9_ caption {\n",
       "  text-align: center;\n",
       "  caption-side: top;\n",
       "  font-size: 150%;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7c6d9_\">\n",
       "  <caption>Table. Total Population by Persons by Race, Ethnicity, Beaumont, TX, 2010.</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Hispanic</th>\n",
       "      <th class=\"col_heading level0 col0\" >0 Not Hispanic or Latino (%)</th>\n",
       "      <th class=\"col_heading level0 col1\" >1 Hispanic or Latino (%)</th>\n",
       "      <th class=\"col_heading level0 col2\" >Total Population by Persons (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Race</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7c6d9_level0_row0\" class=\"row_heading level0 row0\" >1. White</th>\n",
       "      <td id=\"T_7c6d9_row0_col0\" class=\"data row0 col0\" >112,503\t (53.8%)</td>\n",
       "      <td id=\"T_7c6d9_row0_col1\" class=\"data row0 col1\" >19,071\t (44.3%)</td>\n",
       "      <td id=\"T_7c6d9_row0_col2\" class=\"data row0 col2\" >131,574\t (52.2%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c6d9_level0_row1\" class=\"row_heading level0 row1\" >2. Black or African American</th>\n",
       "      <td id=\"T_7c6d9_row1_col0\" class=\"data row1 col0\" >84,502\t (40.4%)</td>\n",
       "      <td id=\"T_7c6d9_row1_col1\" class=\"data row1 col1\" >789\t (1.8%)</td>\n",
       "      <td id=\"T_7c6d9_row1_col2\" class=\"data row1 col2\" >85,291\t (33.8%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c6d9_level0_row2\" class=\"row_heading level0 row2\" >3. American Indian and Alaska Native</th>\n",
       "      <td id=\"T_7c6d9_row2_col0\" class=\"data row2 col0\" >739\t (0.4%)</td>\n",
       "      <td id=\"T_7c6d9_row2_col1\" class=\"data row2 col1\" >642\t (1.5%)</td>\n",
       "      <td id=\"T_7c6d9_row2_col2\" class=\"data row2 col2\" >1,381\t (0.5%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c6d9_level0_row3\" class=\"row_heading level0 row3\" >4. Asian</th>\n",
       "      <td id=\"T_7c6d9_row3_col0\" class=\"data row3 col0\" >8,518\t (4.1%)</td>\n",
       "      <td id=\"T_7c6d9_row3_col1\" class=\"data row3 col1\" >112\t (0.3%)</td>\n",
       "      <td id=\"T_7c6d9_row3_col2\" class=\"data row3 col2\" >8,630\t (3.4%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c6d9_level0_row4\" class=\"row_heading level0 row4\" >5. Native Hawaiian and Other Pacific Islander</th>\n",
       "      <td id=\"T_7c6d9_row4_col0\" class=\"data row4 col0\" >64\t (0.0%)</td>\n",
       "      <td id=\"T_7c6d9_row4_col1\" class=\"data row4 col1\" >19\t (0.0%)</td>\n",
       "      <td id=\"T_7c6d9_row4_col2\" class=\"data row4 col2\" >83\t (0.0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c6d9_level0_row5\" class=\"row_heading level0 row5\" >6. Some Other Race</th>\n",
       "      <td id=\"T_7c6d9_row5_col0\" class=\"data row5 col0\" >210\t (0.1%)</td>\n",
       "      <td id=\"T_7c6d9_row5_col1\" class=\"data row5 col1\" >20,143\t (46.8%)</td>\n",
       "      <td id=\"T_7c6d9_row5_col2\" class=\"data row5 col2\" >20,353\t (8.1%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c6d9_level0_row6\" class=\"row_heading level0 row6\" >7. Two or More Races</th>\n",
       "      <td id=\"T_7c6d9_row6_col0\" class=\"data row6 col0\" >2,710\t (1.3%)</td>\n",
       "      <td id=\"T_7c6d9_row6_col1\" class=\"data row6 col1\" >2,251\t (5.2%)</td>\n",
       "      <td id=\"T_7c6d9_row6_col2\" class=\"data row6 col2\" >4,961\t (2.0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c6d9_level0_row7\" class=\"row_heading level0 row7\" >Total</th>\n",
       "      <td id=\"T_7c6d9_row7_col0\" class=\"data row7 col0\" >209,246\t (100.0%)</td>\n",
       "      <td id=\"T_7c6d9_row7_col1\" class=\"data row7 col1\" >43,027\t (100.0%)</td>\n",
       "      <td id=\"T_7c6d9_row7_col2\" class=\"data row7 col2\" >252,273\t (100.0%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1b6d96d7fc8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PopResultsTable.pop_results_table(\n",
    "                  input_df = prec_df, \n",
    "                  who = \"Total Population by Persons\", \n",
    "                  what = \"by Race, Ethnicity\",\n",
    "                  where = community_name,\n",
    "                  when = \"2010\",\n",
    "                  row_index = \"Race\",\n",
    "                  col_index = 'Hispanic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validate the Person Record File\n",
    "The population count totals for the community will match data collected for the 2010 Decennial Census. This can be confirmed by going to data.census.gov\n",
    "\n",
    "The population by Hispanic will not match exactly but will be fairly close. The data for race, sex, and age by Hispanic requires a random merge and will vary based on the random see used in the algorithm.\n",
    "\n",
    "Total Population by Race and Ethnicity: https://data.census.gov/cedsci/table?g=0500000US48245&tid=DECENNIALSF12010.P5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population Pyramid\n",
    "work on making a population pyramid using the data.\n",
    "\n",
    "#### See Examples:\n",
    "ggplot for python = plotnine:\n",
    "\n",
    "https://towardsdatascience.com/how-to-use-ggplot2-in-python-74ab8adec129\n",
    "\n",
    "My example of a population pyramid using R:\n",
    "\n",
    "Rosenheim, Nathanael. Population Pyramid Data and R Script for the US, States, and Counties 1970 - 2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-01-23. https://doi.org/10.3886/E117081V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(prec_df['randagePCT12'])\n",
    "plt.title(\"Histogram of Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1b6818750c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try distribution plot from figures\n",
    "from pyncoda.ncoda_04a_Figures import *\n",
    "from pyncoda.CommunitySourceData.api_census_gov.acg_00g_prec_datastructure \\\n",
    "    import prec_v300_DataStructure\n",
    "\n",
    "# Distribution Plot\n",
    "income_distribution(prec_df,\n",
    "                        variable = \"randagePCT12\",\n",
    "                        by_variable = \"sex\",\n",
    "                        xlabel_string = \"Age\",\n",
    "                        datastructure = prec_v300_DataStructure,\n",
    "                        communities = communities,\n",
    "                        community = community,\n",
    "                        year = 2010,\n",
    "                        savefile = False,\n",
    "                        notes = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>precid</th>\n",
       "      <th>Tract2010</th>\n",
       "      <th>Block2010</th>\n",
       "      <th>Block2010str</th>\n",
       "      <th>sex</th>\n",
       "      <th>race</th>\n",
       "      <th>hispan</th>\n",
       "      <th>randageP12</th>\n",
       "      <th>agegroupP12</th>\n",
       "      <th>randagePCT12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B482450001011000P0008</td>\n",
       "      <td>48245000101</td>\n",
       "      <td>482450001011000</td>\n",
       "      <td>B482450001011000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B482450001011000P0009</td>\n",
       "      <td>48245000101</td>\n",
       "      <td>482450001011000</td>\n",
       "      <td>B482450001011000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>B482450001011000P0057</td>\n",
       "      <td>48245000101</td>\n",
       "      <td>482450001011000</td>\n",
       "      <td>B482450001011000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>B482450001011000P0058</td>\n",
       "      <td>48245000101</td>\n",
       "      <td>482450001011000</td>\n",
       "      <td>B482450001011000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>B482450001011000P0059</td>\n",
       "      <td>48245000101</td>\n",
       "      <td>482450001011000</td>\n",
       "      <td>B482450001011000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>14</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252130</th>\n",
       "      <td>B482450002002016P0072</td>\n",
       "      <td>48245000200</td>\n",
       "      <td>482450002002016</td>\n",
       "      <td>B482450002002016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252141</th>\n",
       "      <td>B482450001031015P0243</td>\n",
       "      <td>48245000103</td>\n",
       "      <td>482450001031015</td>\n",
       "      <td>B482450001031015</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252178</th>\n",
       "      <td>B482450026005018P0062</td>\n",
       "      <td>48245002600</td>\n",
       "      <td>482450026005018</td>\n",
       "      <td>B482450026005018</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252185</th>\n",
       "      <td>B482450003082020P0861</td>\n",
       "      <td>48245000308</td>\n",
       "      <td>482450003082020</td>\n",
       "      <td>B482450003082020</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252242</th>\n",
       "      <td>B482450003082006P0282</td>\n",
       "      <td>48245000308</td>\n",
       "      <td>482450003082006</td>\n",
       "      <td>B482450003082006</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13059 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       precid    Tract2010        Block2010      Block2010str  \\\n",
       "7       B482450001011000P0008  48245000101  482450001011000  B482450001011000   \n",
       "8       B482450001011000P0009  48245000101  482450001011000  B482450001011000   \n",
       "56      B482450001011000P0057  48245000101  482450001011000  B482450001011000   \n",
       "57      B482450001011000P0058  48245000101  482450001011000  B482450001011000   \n",
       "58      B482450001011000P0059  48245000101  482450001011000  B482450001011000   \n",
       "...                       ...          ...              ...               ...   \n",
       "252130  B482450002002016P0072  48245000200  482450002002016  B482450002002016   \n",
       "252141  B482450001031015P0243  48245000103  482450001031015  B482450001031015   \n",
       "252178  B482450026005018P0062  48245002600  482450026005018  B482450026005018   \n",
       "252185  B482450003082020P0861  48245000308  482450003082020  B482450003082020   \n",
       "252242  B482450003082006P0282  48245000308  482450003082006  B482450003082006   \n",
       "\n",
       "        sex  race  hispan  randageP12  agegroupP12  randagePCT12  \n",
       "7         1     1       0          11            3            14  \n",
       "8         1     1       0          11            3            12  \n",
       "56        2     1       0          12            3            14  \n",
       "57        2     1       0          10            3            11  \n",
       "58        2     1       0          13            3            14  \n",
       "...     ...   ...     ...         ...          ...           ...  \n",
       "252130    1     2       1          12            3            14  \n",
       "252141    2     2       1          11            3            12  \n",
       "252178    2     2       1          11            3            12  \n",
       "252185    2     2       1          11            3            11  \n",
       "252242    2     2       1          10            3            14  \n",
       "\n",
       "[13059 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age80 = (prec_df['randagePCT12'] > 10)\n",
    "age85 = (prec_df['randagePCT12'] < 15)\n",
    "conditions = age80 & age85\n",
    "prec_df.loc[conditions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of all communities available in IN-CORE\n",
    "communities = {'Lumberton_NC' : {\n",
    "                    'community_name' : 'Lumberton, NC',\n",
    "                    'counties' : { \n",
    "                        1 : {'FIPS Code' : '37155', 'Name' : 'Robeson County, NC'}}},                   \n",
    "                'Shelby_TN' : {\n",
    "                    'community_name' : 'Memphis, TN',\n",
    "                    'counties' : { \n",
    "                        1 : {'FIPS Code' : '47157', 'Name' : 'Shelby County, TN'}}},\n",
    "                'Joplin_MO' : {\n",
    "                    'community_name' : 'Joplin, MO',\n",
    "                    'counties' : { \n",
    "                        1 : {'FIPS Code' : '29097', 'Name' : 'Jasper County, MO'},\n",
    "                        2 : {'FIPS Code' : '29145', 'Name' : 'Newton County, MO'}}},\n",
    "                'Seaside_OR' : {\n",
    "                    'community_name' : 'Seaside, OR',\n",
    "                    'counties' : { \n",
    "                        1 : {'FIPS Code' : '41007', 'Name' : 'Clatsop County, OR'}}},                   \n",
    "                'Galveston_TX' : {\n",
    "                    'community_name' : 'Galveston, TX',\n",
    "                    'counties' : { \n",
    "                        1 : {'FIPS Code' : '48167', 'Name' : 'Galveston County, TX'}}},\n",
    "                'Mobile_AL' : {\n",
    "                    'community_name' : 'Mobile, AL',\n",
    "                    'counties' : { \n",
    "                        1 : {'FIPS Code' : '01097', 'Name' : 'Mobile County, AL'}}}                    \n",
    "                }"
   ]
  }
 ],
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  "colab": {
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