SANDAG Sector Comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SANDAG Sector Estimates Analysis\n",
    "This is a calculator that takes in EDD data and aggregates it to specified SANDAG sectors. This also takes in estimates data and aggregates those to SANDAG sectors as well. Then a comparison between the two is drawn. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See Purva's Excel for more information: https://sandag.sharepoint.com/:x:/r/qaqc/_layouts/15/Doc.aspx?sourcedoc=%7BCFD80313-6A49-43E6-B776-50C85CD17DD4%7D&file=EDD_Forecast%20Output%20Industry%20Level%20Jobs%20Comparison_QA.xlsx&action=default&mobileredirect=true&cid=9c4c556c-a447-4a46-8e56-ab5f1c95311a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import urllib.request  # For downloading the xlsx file\n",
    "import pandas as pd\n",
    "from sodapy import Socrata\n",
    "import ssl\n",
    "import sqlalchemy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grabbing EDD Data\n",
    "See documentation here under the 'Employment Controls' section for more information on where the data comes from: https://sandag.sharepoint.com/:w:/r/qaqc/_layouts/15/doc2.aspx?sourcedoc=%7BE29C6BBB-F51B-43D8-87C8-2AF2CB34D5DA%7D&file=MGRABased_input_ABM_2019_process_notes%20-%20Copy.docx&action=default&mobileredirect=true&isSPOFile=1&clickparams=eyJBcHBOYW1lIjoiVGVhbXMtRGVza3RvcCIsIkFwcFZlcnNpb24iOiIyNy8yMjA3MDMwMDgxNSIsIkhhc0ZlZGVyYXRlZFVzZXIiOmZhbHNlfQ%3D%3D&cid=5a7ad9e6-56cc-41b4-b447-1bc8ccf0eff6\n",
    "\n",
    "Documentation stipulates that data is taken from here: https://data.edd.ca.gov/Industry-Information-/Current-Employment-Statistics-CES-Annual-Average-1/pwx8-ztk5/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    }
   ],
   "source": [
    "# Sector Level Data\n",
    "client = Socrata(\"data.edd.ca.gov\", None)\n",
    "results = client.get_all(\"pwx8-ztk5\", area_name='San Diego-Carlsbad MSA', year=2019)\n",
    "results_df = pd.DataFrame.from_records(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab the EDD sectors that we are interested in \n",
    "edd_breakdown = ['Mining and Logging','Total Farm','Construction','Manufacturing','Wholesale Trade','Retail Trade','Utilities','Transportation and Warehousing','Information','Finance and Insurance','Real Estate and Rental and Leasing','Professional, Scientific and Technical S','Management of Companies and Enterprises','Administrative and Support and Waste Ser','Educational Services','Health Care and Social Assistance','Arts, Entertainment, and Recreation','Accommodation','Food Services and Drinking Places','Other Services','Federal Government excluding Department of Defense','Department of Defense','State Government Education','State Government Excluding Education','Local Government Education','Local Government Excluding Education']\n",
    "\n",
    "subset_data = results_df[results_df['industry_title'].isin(edd_breakdown)]\n",
    "subset_data = subset_data[['industry_title', 'current_employment']]\n",
    "edd_data = subset_data.set_index('industry_title').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>industry_title</th>\n",
       "      <th>Total Farm</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>Construction</th>\n",
       "      <th>Mining and Logging</th>\n",
       "      <th>Finance and Insurance</th>\n",
       "      <th>Information</th>\n",
       "      <th>Transportation and Warehousing</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Retail Trade</th>\n",
       "      <th>Wholesale Trade</th>\n",
       "      <th>...</th>\n",
       "      <th>Other Services</th>\n",
       "      <th>Food Services and Drinking Places</th>\n",
       "      <th>Accommodation</th>\n",
       "      <th>Arts, Entertainment, and Recreation</th>\n",
       "      <th>Health Care and Social Assistance</th>\n",
       "      <th>Educational Services</th>\n",
       "      <th>Administrative and Support and Waste Ser</th>\n",
       "      <th>Management of Companies and Enterprises</th>\n",
       "      <th>Professional, Scientific and Technical S</th>\n",
       "      <th>Real Estate and Rental and Leasing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>current_employment</th>\n",
       "      <td>9700</td>\n",
       "      <td>115700</td>\n",
       "      <td>84000</td>\n",
       "      <td>400</td>\n",
       "      <td>46400</td>\n",
       "      <td>23500</td>\n",
       "      <td>29700</td>\n",
       "      <td>4600</td>\n",
       "      <td>145600</td>\n",
       "      <td>44000</td>\n",
       "      <td>...</td>\n",
       "      <td>56400</td>\n",
       "      <td>139400</td>\n",
       "      <td>32100</td>\n",
       "      <td>30200</td>\n",
       "      <td>186400</td>\n",
       "      <td>30200</td>\n",
       "      <td>87000</td>\n",
       "      <td>23800</td>\n",
       "      <td>145000</td>\n",
       "      <td>30200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "industry_title     Total Farm Manufacturing Construction Mining and Logging  \\\n",
       "current_employment       9700        115700        84000                400   \n",
       "\n",
       "industry_title     Finance and Insurance Information  \\\n",
       "current_employment                 46400       23500   \n",
       "\n",
       "industry_title     Transportation and Warehousing Utilities Retail Trade  \\\n",
       "current_employment                          29700      4600       145600   \n",
       "\n",
       "industry_title     Wholesale Trade  ... Other Services  \\\n",
       "current_employment           44000  ...          56400   \n",
       "\n",
       "industry_title     Food Services and Drinking Places Accommodation  \\\n",
       "current_employment                            139400         32100   \n",
       "\n",
       "industry_title     Arts, Entertainment, and Recreation  \\\n",
       "current_employment                               30200   \n",
       "\n",
       "industry_title     Health Care and Social Assistance Educational Services  \\\n",
       "current_employment                            186400                30200   \n",
       "\n",
       "industry_title     Administrative and Support and Waste Ser  \\\n",
       "current_employment                                    87000   \n",
       "\n",
       "industry_title     Management of Companies and Enterprises  \\\n",
       "current_employment                                   23800   \n",
       "\n",
       "industry_title     Professional, Scientific and Technical S  \\\n",
       "current_employment                                   145000   \n",
       "\n",
       "industry_title     Real Estate and Rental and Leasing  \n",
       "current_employment                              30200  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edd_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grabbing Estimates Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#estimates_data = pd.read_csv('C:/Users/cra/OneDrive - San Diego Association of Governments/SANDAG-Sector-Estimates-Analysis/region_ind_DS42.csv')\n",
    "#Adjust the year\n",
    "#estimates_data = estimates_data[estimates_data['year'] == 2020].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# estimates_data = pd.read_csv('C:/Users/cra/OneDrive - San Diego Association of Governments/SANDAG-Sector-Estimates-Analysis/mgra15_region_ind_QA.csv')\n",
    "estimates_data = pd.read_csv('C:/Users/cra/San Diego Association of Governments/SANDAG QA QC - Documents/Projects/2022/2022-58 2019 Base Year Forecast Output QC/data/MGRA13 Updated Data/mgra13_update_region_ind_QA.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Final Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the final dataframe\n",
    "final_df = pd.DataFrame()\n",
    "\n",
    "# Setting up the proper code titles\n",
    "final_df.index = ['Mining, logging, total farm', 'Arts, Entertainment & Recreation', 'Construction', 'Department of Defense', 'Federal Government excluding Department of Defense', 'Health Care and Social Assistance', 'Accomodation', 'Manufacturing', 'Other Services', 'Professional Services', 'Government Education', 'Educational Services', 'Food Services', 'Retail Trade', 'Government Non-education', 'Transportation & Warehousing', 'Utilities', 'Wholesale Trade']\n",
    "\n",
    "# Originally Set the Data to Zero\n",
    "final_df['Estimates Data'] = 0\n",
    "final_df['EDD Data'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filling in the final dataframe with  the correct values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mining, logging, total farm \n",
    "final_df['Estimates Data']['Mining, logging, total farm'] = int(estimates_data['emp_ag'])\n",
    "final_df['EDD Data']['Mining, logging, total farm'] = int(edd_data['Mining and Logging']) + int(edd_data['Total Farm'])\n",
    "\n",
    "# Arts, Entertainment & Recreation\n",
    "final_df['Estimates Data']['Arts, Entertainment & Recreation'] = int(estimates_data['emp_amusement'])\n",
    "final_df['EDD Data']['Arts, Entertainment & Recreation'] = int(edd_data['Arts, Entertainment, and Recreation'])\n",
    "\n",
    "# Construction\n",
    "final_df['Estimates Data']['Construction'] = int(estimates_data['emp_const_bldg_office']) + int(estimates_data['emp_const_bldg_prod']) + int(estimates_data['emp_const_non_bldg_office']) + int(estimates_data['emp_const_non_bldg_prod'])\n",
    "final_df['EDD Data']['Construction'] = int(edd_data['Construction'])\n",
    "\n",
    "# Department of Defence \n",
    "final_df['Estimates Data']['Department of Defense'] = int(estimates_data['emp_fed_mil'])\n",
    "final_df['EDD Data']['Department of Defense'] = int(edd_data['Department of Defense']) # I don't see a number 27\n",
    "\n",
    "\n",
    "# Federal Government excluding Department of Defense\n",
    "final_df['Estimates Data']['Federal Government excluding Department of Defense'] = int(estimates_data['emp_fed_non_mil'])\n",
    "final_df['EDD Data']['Federal Government excluding Department of Defense'] = int(edd_data['Federal Government excluding Department of Defense'])\n",
    "\n",
    "# Health Care & Social Assistance\n",
    "final_df['Estimates Data']['Health Care and Social Assistance'] = int(estimates_data['emp_health'])\n",
    "final_df['EDD Data']['Health Care and Social Assistance'] = int(edd_data['Health Care and Social Assistance'])\n",
    "\n",
    "# Accommodation\n",
    "final_df['Estimates Data']['Accomodation'] = int(estimates_data['emp_hotel'])\n",
    "final_df['EDD Data']['Accomodation'] = int(edd_data['Accommodation'])\n",
    "\n",
    "\n",
    "# Manufacturing\n",
    "final_df['Estimates Data']['Manufacturing'] = int(estimates_data['emp_mfg_office']) + int(estimates_data['emp_mfg_prod'])\n",
    "final_df['EDD Data']['Manufacturing'] = int(edd_data['Manufacturing']) \n",
    "\n",
    "# Other Services\n",
    "final_df['Estimates Data']['Other Services'] = int(estimates_data['emp_personal_svcs_office']) + int(estimates_data['emp_personal_svcs_retail'])\n",
    "final_df['EDD Data']['Other Services'] = int(edd_data['Other Services'])\n",
    "\n",
    "# Professional Services \n",
    "final_df['Estimates Data']['Professional Services'] = int(estimates_data['emp_prof_bus_svcs']) + int(estimates_data['emp_prof_bus_svcs_bldg_maint'])\n",
    "final_df['EDD Data']['Professional Services'] = int(edd_data['Information']) + int(edd_data['Finance and Insurance']) + int(edd_data['Real Estate and Rental and Leasing']) + int(edd_data['Professional, Scientific and Technical S']) + int(edd_data['Management of Companies and Enterprises']) + int(edd_data['Administrative and Support and Waste Ser'])\n",
    "\n",
    "# Government Education - Spelling\n",
    "final_df['Estimates Data']['Government Education'] = int(estimates_data['emp_public_ed'])\n",
    "final_df['EDD Data']['Government Education'] = int(edd_data['State Government Education']) + int(edd_data['Local Government Education'])\n",
    "\n",
    "# Educational Services\n",
    "final_df['Estimates Data']['Educational Services'] = int(estimates_data['emp_pvt_ed_k12']) + int(estimates_data['emp_pvt_ed_post_k12_oth'])\n",
    "final_df['EDD Data']['Educational Services'] = int(edd_data['Educational Services'])\n",
    "\n",
    "\n",
    "# Food Services\n",
    "final_df['Estimates Data']['Food Services'] = int(estimates_data['emp_restaurant_bar'])\n",
    "final_df['EDD Data']['Food Services'] = int(edd_data['Food Services and Drinking Places']) #This is an assumption, not explicitly stated\n",
    "\n",
    "# Retail Trade \n",
    "final_df['Estimates Data']['Retail Trade'] = int(estimates_data['emp_retail'])\n",
    "final_df['EDD Data']['Retail Trade'] = int(edd_data['Retail Trade'])\n",
    "\n",
    "# Government Non-eduction\n",
    "final_df['Estimates Data']['Government Non-education'] = int(estimates_data['emp_state_local_gov_blue']) + int(estimates_data['emp_state_local_gov_ent']) + int(estimates_data['emp_state_local_gov_white'])\n",
    "final_df['EDD Data']['Government Non-education'] = int(edd_data['State Government Excluding Education']) + int(edd_data['Local Government Excluding Education'])\n",
    "\n",
    "# Transportation and Warehousing\n",
    "final_df['Estimates Data']['Transportation & Warehousing'] = int(estimates_data['emp_trans'])\n",
    "final_df['EDD Data']['Transportation & Warehousing'] = int(edd_data['Transportation and Warehousing'])\n",
    "\n",
    "# Utilities\n",
    "final_df['Estimates Data']['Utilities'] = int(estimates_data['emp_utilities_office']) + int(estimates_data['emp_utilities_prod'])\n",
    "final_df['EDD Data']['Utilities'] = int(edd_data['Utilities'])\n",
    "\n",
    "# Wholesale Trade\n",
    "final_df['Estimates Data']['Wholesale Trade'] = int(estimates_data['emp_whsle_whs'])\n",
    "final_df['EDD Data']['Wholesale Trade'] = int(edd_data['Wholesale Trade'])\n",
    "\n",
    "\n",
    "final_df['Diff'] = final_df['Estimates Data'] - final_df['EDD Data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.loc['Sum'] = [sum(final_df['Estimates Data']), sum(final_df['EDD Data']), (sum(final_df['Estimates Data']) - sum(final_df['EDD Data']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimates Data</th>\n",
       "      <th>EDD Data</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mining, logging, total farm</th>\n",
       "      <td>9905</td>\n",
       "      <td>10100</td>\n",
       "      <td>-195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arts, Entertainment &amp; Recreation</th>\n",
       "      <td>49907</td>\n",
       "      <td>30200</td>\n",
       "      <td>19707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Construction</th>\n",
       "      <td>92078</td>\n",
       "      <td>84000</td>\n",
       "      <td>8078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department of Defense</th>\n",
       "      <td>129474</td>\n",
       "      <td>23700</td>\n",
       "      <td>105774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Federal Government excluding Department of Defense</th>\n",
       "      <td>11454</td>\n",
       "      <td>23900</td>\n",
       "      <td>-12446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Care and Social Assistance</th>\n",
       "      <td>215961</td>\n",
       "      <td>186400</td>\n",
       "      <td>29561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accomodation</th>\n",
       "      <td>33604</td>\n",
       "      <td>32100</td>\n",
       "      <td>1504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing</th>\n",
       "      <td>115687</td>\n",
       "      <td>115700</td>\n",
       "      <td>-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Services</th>\n",
       "      <td>70566</td>\n",
       "      <td>56400</td>\n",
       "      <td>14166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professional Services</th>\n",
       "      <td>398762</td>\n",
       "      <td>355900</td>\n",
       "      <td>42862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Government Education</th>\n",
       "      <td>0</td>\n",
       "      <td>113100</td>\n",
       "      <td>-113100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Educational Services</th>\n",
       "      <td>146388</td>\n",
       "      <td>30200</td>\n",
       "      <td>116188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food Services</th>\n",
       "      <td>140070</td>\n",
       "      <td>139400</td>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail Trade</th>\n",
       "      <td>158246</td>\n",
       "      <td>145600</td>\n",
       "      <td>12646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Government Non-education</th>\n",
       "      <td>46389</td>\n",
       "      <td>87900</td>\n",
       "      <td>-41511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation &amp; Warehousing</th>\n",
       "      <td>42219</td>\n",
       "      <td>29700</td>\n",
       "      <td>12519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>7210</td>\n",
       "      <td>4600</td>\n",
       "      <td>2610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesale Trade</th>\n",
       "      <td>46625</td>\n",
       "      <td>44000</td>\n",
       "      <td>2625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sum</th>\n",
       "      <td>1714545</td>\n",
       "      <td>1512900</td>\n",
       "      <td>201645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Estimates Data  EDD Data  \\\n",
       "Mining, logging, total farm                                   9905     10100   \n",
       "Arts, Entertainment & Recreation                             49907     30200   \n",
       "Construction                                                 92078     84000   \n",
       "Department of Defense                                       129474     23700   \n",
       "Federal Government excluding Department of Defense           11454     23900   \n",
       "Health Care and Social Assistance                           215961    186400   \n",
       "Accomodation                                                 33604     32100   \n",
       "Manufacturing                                               115687    115700   \n",
       "Other Services                                               70566     56400   \n",
       "Professional Services                                       398762    355900   \n",
       "Government Education                                             0    113100   \n",
       "Educational Services                                        146388     30200   \n",
       "Food Services                                               140070    139400   \n",
       "Retail Trade                                                158246    145600   \n",
       "Government Non-education                                     46389     87900   \n",
       "Transportation & Warehousing                                 42219     29700   \n",
       "Utilities                                                     7210      4600   \n",
       "Wholesale Trade                                              46625     44000   \n",
       "Sum                                                        1714545   1512900   \n",
       "\n",
       "                                                      Diff  \n",
       "Mining, logging, total farm                           -195  \n",
       "Arts, Entertainment & Recreation                     19707  \n",
       "Construction                                          8078  \n",
       "Department of Defense                               105774  \n",
       "Federal Government excluding Department of Defense  -12446  \n",
       "Health Care and Social Assistance                    29561  \n",
       "Accomodation                                          1504  \n",
       "Manufacturing                                          -13  \n",
       "Other Services                                       14166  \n",
       "Professional Services                                42862  \n",
       "Government Education                               -113100  \n",
       "Educational Services                                116188  \n",
       "Food Services                                          670  \n",
       "Retail Trade                                         12646  \n",
       "Government Non-education                            -41511  \n",
       "Transportation & Warehousing                         12519  \n",
       "Utilities                                             2610  \n",
       "Wholesale Trade                                       2625  \n",
       "Sum                                                 201645  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_df.to_excel('C:/Users/cra/San Diego Association of Governments/SANDAG QA QC - Documents/Projects/2022/2022-58 2019 Base Year Forecast Output QC/Output/mgra13_EDD_SANDAG_Sector_Diff.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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