27567 lines
1.0 MiB
27567 lines
1.0 MiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "4235cfaf-5e1d-49ac-bab9-4f1dc8b057d7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"import pandas as pd\n",
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"import textwrap\n",
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"import plotly.graph_objects as go\n",
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"import plotly.express as px\n",
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"import kaleido\n",
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"import addfips"
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]
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},
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{
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"cell_type": "markdown",
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"id": "34c4ded6-fc29-4754-a7f7-d69559d4464f",
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"metadata": {},
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"source": [
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"# Obtaining NAICS data for PA\n",
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"---\n",
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"To make the 1.1 Section of the desk review we first need to obtain the cencus business data for PA\n",
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"\n",
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"The below function obtains the 2022 census (Most recent data year at time of writing) and returns it as a 2D array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "d52921ed-3689-4aec-a841-1aefc34c7fa9",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_naics_data(api_url = \"https://api.census.gov/data/2022/ecnbasic?get=group(EC2200BASIC)&NAICS2022=pseudo(N0200.00)&ucgid=0400000US42\"):\n",
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" try:\n",
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" census_naics_json = requests.get(api_url)\n",
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" \n",
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" if census_naics_json.status_code == 200:\n",
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" table_data = census_naics_json.json()\n",
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" return table_data\n",
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" else:\n",
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" return []\n",
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" \n",
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" except requests.exceptions.RequestException as e:\n",
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" print(\"Failed to get NAICS census data from the API. Verify that the url is still correct.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "09f5a056-f219-4a28-b3b1-2a5763508cd5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[['EMP', 'EMP_F', 'EMP_IMP', 'EMP_IMP_F', 'ESTAB', 'ESTAB_F', 'FIRM', 'FIRM_F', 'GEO_ID', 'GEO_ID_F', 'NAME', 'NAICS2022', 'NAICS2022_F', 'NAICS2022_LABEL', 'PAYANN', 'PAYANN_F', 'PAYANN_IMP', 'PAYANN_IMP_F', 'PAYQTR1', 'PAYQTR1_F', 'RCPTOT', 'RCPTOT_F', 'RCPTOT_IMP', 'RCPTOT_IMP_F', 'TAXSTAT', 'TAXSTAT_LABEL', 'TYPOP', 'TYPOP_LABEL', 'YEAR', 'NAICS2022', 'ucgid'], ['19792', None, '0', '2', '832', None, '614', None, '0400000US42', None, 'Pennsylvania', '21', None, 'Mining, quarrying, and oil and gas extraction', '1784460', None, '0', '2', '445665', None, '44369022', None, '0', '0', '00', 'All establishments', '00', 'All establishments', '2022', '21', '0400000US42'], ['30844', None, '0', '1', '917', None, '252', None, '0400000US42', None, 'Pennsylvania', '22', None, 'Utilities', '3538744', None, '0', '0', '1078106', None, '32259552', None, '0', '0', '00', 'All establishments', '00', 'All establishments', '2022', '22', '0400000US42'], ['261765', None, '0', '2', '28792', None, '28433', None, '0400000US42', None, 'Pennsylvania', '23', None, 'Construction', '18896978', None, '0', '0', '4007954', None, '93996998', None, '0', '1', '00', 'All establishments', '00', 'All establishments', '2022', '23', '0400000US42'], ['563035', None, '0', '2', '13377', None, '11984', None, '0400000US42', None, 'Pennsylvania', '31-33', None, 'Manufacturing', '36353647', None, '0', '2', '8835257', None, '262564512', None, '0', '3', '00', 'All establishments', '00', 'All establishments', '2022', '31-33', '0400000US42'], ['252741', None, '0', '3', '12585', None, '9750', None, '0400000US42', None, 'Pennsylvania', '42', None, 'Wholesale trade', '21162185', None, '0', '4', '5607679', None, '411318239', None, '0', '1', '00', 'All establishments', '00', 'All establishments', '2022', '42', '0400000US42'], ['202600', None, '0', '3', '10632', None, '8321', None, '0400000US42', None, 'Pennsylvania', '42', None, 'Wholesale trade', '15143956', None, '0', '3', '3728627', None, '259322722', None, '0', '1', '00', 'All establishments', '10', \"Merchant wholesalers, except manufacturers' sales branches and offices\", '2022', '42', '0400000US42'], ['40420', None, '0', '6', '732', None, '330', None, '0400000US42', None, 'Pennsylvania', '42', None, 'Wholesale trade', '5294916', None, '0', '7', '1708821', None, '121491259', None, '0', '1', '00', 'All establishments', '20', \"Manufacturers' sales branches and offices\", '2022', '42', '0400000US42'], ['689445', None, '0', '2', '40165', None, '24552', None, '0400000US42', None, 'Pennsylvania', '44-45', None, 'Retail trade', '22798490', None, '0', '2', '5482917', None, '301886383', None, '0', '2', '00', 'All establishments', '00', 'All establishments', '2022', '44-45', '0400000US42'], ['272785', None, '0', '2', '10858', None, '8798', None, '0400000US42', None, 'Pennsylvania', '48-49', '107', 'Transportation and warehousing', '13976679', None, '0', '2', '3337124', None, '42748848', None, '0', '3', '00', 'All establishments', '00', 'All establishments', '2022', '48-49', '0400000US42'], ['107284', None, '0', '3', '5645', None, '2877', None, '0400000US42', None, 'Pennsylvania', '51', None, 'Information', '10240723', None, '0', '3', '2874268', None, '47462326', None, '0', '3', '00', 'All establishments', '00', 'All establishments', '2022', '51', '0400000US42'], ['319889', None, '0', '2', '17692', None, '8906', None, '0400000US42', None, 'Pennsylvania', '52', '115', 'Finance and insurance', '32714999', None, '0', '2', '10239224', None, '270729542', None, '0', '0', '00', 'All establishments', '00', 'All establishments', '2022', '52', '0400000US42'], ['77732', None, '0', '2', '11674', None, '8702', None, '0400000US42', None, 'Pennsylvania', '53', None, 'Real estate and rental and leasing', '4747740', None, '0', '1', '1182233', None, '27046613', None, '0', '2', '00', 'All establishments', '00', 'All establishments', '2022', '53', '0400000US42'], ['391239', None, '0', '2', '30362', None, '27665', None, '0400000US42', None, 'Pennsylvania', '54', None, 'Professional, scientific, and technical services', '35025855', None, '0', '2', '8635386', None, '92640000', None, '0', '3', '00', 'All establishments', '00', 'All establishments', '2022', '54', '0400000US42'], ['385749', None, '0', '2', '30161', None, '27552', None, '0400000US42', None, 'Pennsylvania', '54', None, 'Professional, scientific, and technical services', '34609148', None, '0', '2', '8540229', None, '91663124', None, '0', '3', 'T', 'Establishments subject to federal income tax', '00', 'All establishments', '2022', '54', '0400000US42'], ['5490', None, '0', '2', '201', None, '118', None, '0400000US42', None, 'Pennsylvania', '54', None, 'Professional, scientific, and technical services', '416707', None, '0', '1', '95157', None, '976876', None, '0', '3', 'Y', 'Establishments exempt from federal income tax', '00', 'All establishments', '2022', '54', '0400000US42'], ['243649', None, '0', '3', '2356', None, '1369', None, '0400000US42', None, 'Pennsylvania', '55', None, 'Management of companies and enterprises', '22253581', None, '0', '3', '6614090', None, '2963566', None, '0', '3', '00', 'All establishments', '00', 'All establishments', '2022', '55', '0400000US42'], ['362904', None, '0', '2', '16769', None, '14161', None, '0400000US42', None, 'Pennsylvania', '56', None, 'Administrative and support and waste management and remediation services', '16073022', None, '0', '2', '3744495', None, '41378855', None, '0', '3', '00', 'All establishments', '00', 'All establishments', '2022', '56', '0400000US42'], ['26627', None, '0', '1', '2860', None, '2720', None, '0400000US42', None, 'Pennsylvania', '61', '117', 'Educational services', '957785', None, '0', '1', '222531', None, '2767178', None, '0', '2', '00', 'All establishments', '00', 'All establishments', '2022', '61', '0400000US42'], ['18878', None, '0', '2', '2270', None, '2163', None, '0400000US42', None, 'Pennsylvania', '61', '117', 'Educational services', '600188', None, '0', '2', '137158', None, '1844599', None, '0', '3', 'T', 'Establishments subject to federal income tax', '00', 'All establishments', '2022', '61', '0400000US42'], ['7749', None, '0', '1', '590', None, '558', None, '0400000US42', None, 'Pennsylvania', '61', '117', 'Educational services', '357597', None, '0', '1', '85373', None, '922579', None, '0', '1', 'Y', 'Establishments exempt from federal income tax', '00', 'All establishments', '2022', '61', '0400000US42'], ['1109625', None, '0', '2', '39375', None, '23841', None, '0400000US42', None, 'Pennsylvania', '62', None, 'Health care and social assistance', '61823458', None, '0', '2', '14423975', None, '154216328', None, '0', '4', '00', 'All establishments', '00', 'All establishments', '2022', '62', '0400000US42'], ['585444', None, '0', '2', '30646', None, '20539', None, '0400000US42', None, 'Pennsylvania', '62', None, 'Health care and social assistance', '32274263', None, '0', '2', '7491659', None, '74148150', None, '0', '4', 'T', 'Establishments subject to federal income tax', '00', 'All establishments', '2022', '62', '0400000US42'], ['524181', None, '0', '3', '8729', None, '3414', None, '0400000US42', None, 'Pennsylvania', '62', None, 'Health care and social assistance', '29549195', None, '0', '3', '6932316', None, '80068178', None, '0', '4', 'Y', 'Establishments exempt from federal income tax', '00', 'All establishments', '2022', '62', '0400000US42'], ['100712', None, '0', '1', '5076', None, '4693', None, '0400000US42', None, 'Pennsylvania', '71', None, 'Arts, entertainment, and recreation', '4172509', None, '0', '0', '881024', None, '13079147', None, '0', '1', '00', 'All establishments', '00', 'All establishments', '2022', '71', '0400000US42'], ['69561', None, '0', '2', '3762', None, '3501', None, '0400000US42', None, 'Pennsylvania', '71', None, 'Arts, entertainment, and recreation', '3218925', None, '0', '1', '684522', None, '10360135', None, '0', '1', 'T', 'Establishments subject to federal income tax', '00', 'All establishments', '2022', '71', '0400000US42'], ['31151', None, '0', '1', '1314', None, '1194', None, '0400000US42', None, 'Pennsylvania', '71', None, 'Arts, entertainment, and recreation', '953584', None, '0', '0', '196502', None, '2719012', None, '0', '0', 'Y', 'Establishments exempt from federal income tax', '00', 'All establishments', '2022', '71', '0400000US42'], ['462379', None, '0', '1', '29042', None, '23281', None, '0400000US42', None, 'Pennsylvania', '72', None, 'Accommodation and food services', '9724441', None, '0', '1', '2193189', None, '34888736', None, '0', '2', '00', 'All establishments', '00', 'All establishments', '2022', '72', '0400000US42'], ['162373', None, '0', '1', '26781', None, '24498', None, '0400000US42', None, 'Pennsylvania', '81', '119', 'Other services (except public administration)', '6204798', None, '0', '1', '1420492', None, '40428262', None, '0', '0', '00', 'All establishments', '00', 'All establishments', '2022', '81', '0400000US42'], ['122551', None, '0', '1', '22075', None, '19955', None, '0400000US42', None, 'Pennsylvania', '81', '119', 'Other services (except public administration)', '4422834', None, '0', '1', '1003809', None, '14823996', None, '0', '2', 'T', 'Establishments subject to federal income tax', '00', 'All establishments', '2022', '81', '0400000US42'], ['39822', None, '0', '0', '4706', None, '4548', None, '0400000US42', None, 'Pennsylvania', '81', '119', 'Other services (except public administration)', '1781964', None, '0', '0', '416683', None, '25604266', None, '0', '0', 'Y', 'Establishments exempt from federal income tax', '00', 'All establishments', '2022', '81', '0400000US42']]\n"
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]
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}
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],
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"source": [
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"naics_data = get_naics_data()\n",
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"print(naics_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "d30062d9-d88f-4bf8-8f62-65030aabae86",
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"metadata": {},
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"outputs": [],
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"source": [
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"column_names = naics_data[0]\n",
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"records = naics_data[1:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "6818b1ee-87cb-491d-b922-98f7352be74b",
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"metadata": {},
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"outputs": [],
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"source": [
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"naics_df = pd.DataFrame(records, columns=column_names)\n",
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"# Convert the establishments column into an int so we can sum it\n",
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"naics_df[\"ESTAB\"] = naics_df[\"ESTAB\"].map(int)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d2cb0774-2a62-4332-b2d7-0edfd65edc22",
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"metadata": {},
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"source": [
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"# Obtaining data for NAICS codes 92 and 11\n",
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"---\n",
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"The main US Census data does not cover codes 92 (goverment operated institutions) and 11 (agriculture)\n",
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"\n",
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"Government institutions are left out as the census is meant to cover only the private sector. Agriculture is left out as it is considered the responsability of the USDA\n",
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"\n",
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"Therefore we need to query both of these institutions to get a count of establishments\n",
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"\n",
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"Developer documentation here: https://www.bls.gov/developers/api_python.htm#python2"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3d37f711-37c3-4e0e-9bc2-05f12e483f6d",
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"metadata": {},
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"source": [
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"### Get the code 92 data from the BLS:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "1c8228fc-015e-468b-82ae-6b7340eea6cd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Data obtained from http://data.bls.gov/cew/data/api/2022/a/area/42000.csv\n",
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"5047 NAICS 92 organizations\n"
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]
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}
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],
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"source": [
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"import urllib \n",
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"import urllib.request\n",
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"import io\n",
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"# 42000 is the code for PA\n",
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"def get_bls_public_admin_data(year, area_code=\"42000\", api_url=\"http://data.bls.gov/cew/data/api/[YEAR]/a/area/[AREA].csv\"):\n",
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" api_url = api_url.replace(\"[YEAR]\",year)\n",
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" api_url = api_url.replace(\"[AREA]\",area_code.upper())\n",
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" \n",
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" httpStream = urllib.request.urlopen(api_url)\n",
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" csv = httpStream.read()\n",
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" httpStream.close()\n",
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" \n",
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" print(\"Data obtained from\", api_url)\n",
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" return pd.read_csv(io.StringIO(csv.decode('UTF-8')))\n",
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" \n",
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"\n",
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"bls_df = get_bls_public_admin_data(\"2022\")\n",
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"\n",
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"# Summing here because there will be 3 sums within this data. One for local, state, and federal institutions\n",
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"naics_92_count = bls_df[bls_df[\"industry_code\"].str.strip() == \"92\"]['annual_avg_estabs'].sum()\n",
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"bls_df.to_csv(\"bls_naics_data.csv\")\n",
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"print(naics_92_count, \"NAICS 92 organizations\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eab31e30-c128-47ee-9d42-53ef852eb6bf",
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"metadata": {},
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"source": [
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"### Get the code 11 data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "4366e29e-e8d3-417f-bd27-32416fb26eea",
|
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"metadata": {},
|
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"https://quickstats.nass.usda.gov/api/api_GET/?key=72A4453A-E4CB-3D1C-BF42-03B6FAF9E7E6&source_desc=CENSUS§or_desc=ECONOMICS&group_desc=FARMS+%26+LAND+%26+ASSETS&commodity_desc=FARM+OPERATIONS&unit_desc=OPERATIONS&agg_level_desc=STATE&state_alpha=PA&domain_desc=TOTAL&short_desc=FARM+OPERATIONS+-+NUMBER+OF+OPERATIONS&year=2022&format=csv\n",
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"49053 farm operations in PA\n"
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]
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}
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],
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"source": [
|
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"import requests\n",
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"import urllib.parse\n",
|
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"import io\n",
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"\n",
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"def get_farm_operations():\n",
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" API_KEY = \"72A4453A-E4CB-3D1C-BF42-03B6FAF9E7E6\"\n",
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" SOURCE_DESC = \"CENSUS\"\n",
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" SECTOR_DESC = \"ECONOMICS\"\n",
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" GROUP_DESC = \"FARMS & LAND & ASSETS\"\n",
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" COMMODITY_DESC = \"FARM OPERATIONS\"\n",
|
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" UNIT_DESC = \"OPERATIONS\"\n",
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" AGG_LEVEL = \"STATE\"\n",
|
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" STATE_ALPHA = \"PA\"\n",
|
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" DOMAIN_DESC = \"TOTAL\"\n",
|
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" SHORT_DESC = \"FARM OPERATIONS - NUMBER OF OPERATIONS\"\n",
|
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" YEAR = \"2022\"\n",
|
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" \n",
|
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" api_url = f\"https://quickstats.nass.usda.gov/api/api_GET/?key={API_KEY}&source_desc={SOURCE_DESC}§or_desc={SECTOR_DESC}&group_desc={urllib.parse.quote_plus(GROUP_DESC)}&commodity_desc={urllib.parse.quote_plus(COMMODITY_DESC)}&unit_desc={UNIT_DESC}&agg_level_desc={AGG_LEVEL}&state_alpha={STATE_ALPHA}&domain_desc={DOMAIN_DESC}&short_desc={urllib.parse.quote_plus(SHORT_DESC)}&year={YEAR}&format=csv\"\n",
|
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" print(api_url)\n",
|
|
" result = requests.get(api_url)\n",
|
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"\n",
|
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" if result.status_code == 200:\n",
|
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" return pd.read_csv(io.StringIO(result.text))\n",
|
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" else:\n",
|
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" raise Exception(\"Could not query quickstats API, check the url!\")\n",
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"\n",
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"naics_11_df = get_farm_operations()\n",
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"\n",
|
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"naics_11_count = int(str(naics_11_df.iloc[0][\"Value\"]).replace(\",\", \"\"))\n",
|
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"print(naics_11_count, \" farm operations in PA\")"
|
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]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "e36493e8-1df6-4a63-880b-a1a9b7592662",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
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"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
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"465076 Total businesses in PA recorded in the census\n"
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]
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}
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],
|
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"source": [
|
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"# This is the column containing the count of establishments in each NAICS code\n",
|
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"total_establishments = naics_df[\"ESTAB\"].sum() + naics_92_count + naics_11_count\n",
|
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"print(total_establishments, \"Total businesses in PA recorded in the census\")"
|
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]
|
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},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "21911f6d-80a8-46e9-8e67-d100950a1af9",
|
|
"metadata": {},
|
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"source": [
|
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"# Aggregating NAICS establishment counts\n",
|
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"---\n",
|
|
"There are multiple records for each NAICS code as there are subcategories. We do not care about those so I will group and sum them"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "5867ff9e-052f-44aa-8961-a74dfad6b586",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
|
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>NAICS_CODE</th>\n",
|
|
" <th>ESTAB</th>\n",
|
|
" <th>NAICS2022_LABEL</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>92</td>\n",
|
|
" <td>5047</td>\n",
|
|
" <td>Government Institutions (local, state, and fed...</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>81</td>\n",
|
|
" <td>53562</td>\n",
|
|
" <td>Other services (except public administration)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>72</td>\n",
|
|
" <td>29042</td>\n",
|
|
" <td>Accommodation and food services</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>71</td>\n",
|
|
" <td>10152</td>\n",
|
|
" <td>Arts, entertainment, and recreation</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>62</td>\n",
|
|
" <td>78750</td>\n",
|
|
" <td>Health care and social assistance</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>61</td>\n",
|
|
" <td>5720</td>\n",
|
|
" <td>Educational services</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>56</td>\n",
|
|
" <td>16769</td>\n",
|
|
" <td>Administrative and support and waste managemen...</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>55</td>\n",
|
|
" <td>2356</td>\n",
|
|
" <td>Management of companies and enterprises</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>54</td>\n",
|
|
" <td>60724</td>\n",
|
|
" <td>Professional, scientific, and technical services</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>53</td>\n",
|
|
" <td>11674</td>\n",
|
|
" <td>Real estate and rental and leasing</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>52</td>\n",
|
|
" <td>17692</td>\n",
|
|
" <td>Finance and insurance</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>51</td>\n",
|
|
" <td>5645</td>\n",
|
|
" <td>Information</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>48-49</td>\n",
|
|
" <td>10858</td>\n",
|
|
" <td>Transportation and warehousing</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>44-45</td>\n",
|
|
" <td>40165</td>\n",
|
|
" <td>Retail trade</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>42</td>\n",
|
|
" <td>23949</td>\n",
|
|
" <td>Wholesale trade</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>31-33</td>\n",
|
|
" <td>13377</td>\n",
|
|
" <td>Manufacturing</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>23</td>\n",
|
|
" <td>28792</td>\n",
|
|
" <td>Construction</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>22</td>\n",
|
|
" <td>917</td>\n",
|
|
" <td>Utilities</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>21</td>\n",
|
|
" <td>832</td>\n",
|
|
" <td>Mining, quarrying, and oil and gas extraction</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>11</td>\n",
|
|
" <td>49053</td>\n",
|
|
" <td>Farm Operations</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>Missing NAICS value</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" NAICS_CODE ESTAB NAICS2022_LABEL\n",
|
|
"18 92 5047 Government Institutions (local, state, and fed...\n",
|
|
"17 81 53562 Other services (except public administration)\n",
|
|
"16 72 29042 Accommodation and food services\n",
|
|
"15 71 10152 Arts, entertainment, and recreation\n",
|
|
"14 62 78750 Health care and social assistance\n",
|
|
"13 61 5720 Educational services\n",
|
|
"12 56 16769 Administrative and support and waste managemen...\n",
|
|
"11 55 2356 Management of companies and enterprises\n",
|
|
"10 54 60724 Professional, scientific, and technical services\n",
|
|
"9 53 11674 Real estate and rental and leasing\n",
|
|
"8 52 17692 Finance and insurance\n",
|
|
"7 51 5645 Information\n",
|
|
"6 48-49 10858 Transportation and warehousing\n",
|
|
"5 44-45 40165 Retail trade\n",
|
|
"4 42 23949 Wholesale trade\n",
|
|
"3 31-33 13377 Manufacturing\n",
|
|
"2 23 28792 Construction\n",
|
|
"1 22 917 Utilities\n",
|
|
"0 21 832 Mining, quarrying, and oil and gas extraction\n",
|
|
"19 11 49053 Farm Operations\n",
|
|
"20 0 0 Missing NAICS value"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create a new NAICS code column as there are two in the data for whatever reason\n",
|
|
"naics_df[\"NAICS_CODE\"] = naics_df[\"NAICS2022\"].iloc[:, 0]\n",
|
|
"\n",
|
|
"# Group the records by their NAICS_CODE and name, then aggregate the sums for the groups\n",
|
|
"naics_df = naics_df.groupby(\n",
|
|
" ['NAICS_CODE']\n",
|
|
").agg({\"ESTAB\": \"sum\", \"NAICS2022_LABEL\": \"first\"}).reset_index()\n",
|
|
"\n",
|
|
"# Append the NAICS 92 data\n",
|
|
"\n",
|
|
"naics_92_data = pd.DataFrame({\n",
|
|
" 'NAICS_CODE': [\"92\"],\n",
|
|
" 'NAICS2022_LABEL': [\"Government Institutions (local, state, and federal)\"],\n",
|
|
" 'ESTAB': [naics_92_count]\n",
|
|
"})\n",
|
|
"\n",
|
|
"naics_11_data = pd.DataFrame({\n",
|
|
" 'NAICS_CODE': [\"11\"],\n",
|
|
" 'NAICS2022_LABEL': [\"Farm Operations\"],\n",
|
|
" 'ESTAB': [naics_11_count]\n",
|
|
"})\n",
|
|
"\n",
|
|
"# So we have a sentinal value that means no value. NAICS 0 does not exist\n",
|
|
"missing_data = pd.DataFrame({\n",
|
|
" 'NAICS_CODE': [\"0\"],\n",
|
|
" 'NAICS2022_LABEL': [\"Missing NAICS value\"],\n",
|
|
" 'ESTAB': [0]\n",
|
|
"})\n",
|
|
"\n",
|
|
"naics_df = pd.concat([naics_df, naics_92_data, naics_11_data, missing_data], ignore_index=True)\n",
|
|
"naics_df = naics_df.sort_values(by='NAICS_CODE', ascending=False)\n",
|
|
"naics_df.head(60)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8a59e45d-d2cb-41ea-83c8-4879a10c9451",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Adding a percentage to each NAICS code\n",
|
|
"---\n",
|
|
"Now that we have a total count for the number of all establishments, we need to calculate the percentage each code makes up of the total"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "63482154-c2b7-42da-a93e-b4b44c6a443f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
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" .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>NAICS_CODE</th>\n",
|
|
" <th>ESTAB</th>\n",
|
|
" <th>NAICS2022_LABEL</th>\n",
|
|
" <th>Percentage</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>62</td>\n",
|
|
" <td>78750</td>\n",
|
|
" <td>Health care and social assistance</td>\n",
|
|
" <td>16.932716</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>54</td>\n",
|
|
" <td>60724</td>\n",
|
|
" <td>Professional, scientific, and technical services</td>\n",
|
|
" <td>13.056791</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>81</td>\n",
|
|
" <td>53562</td>\n",
|
|
" <td>Other services (except public administration)</td>\n",
|
|
" <td>11.516827</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>11</td>\n",
|
|
" <td>49053</td>\n",
|
|
" <td>Farm Operations</td>\n",
|
|
" <td>10.547308</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>44-45</td>\n",
|
|
" <td>40165</td>\n",
|
|
" <td>Retail trade</td>\n",
|
|
" <td>8.636223</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>72</td>\n",
|
|
" <td>29042</td>\n",
|
|
" <td>Accommodation and food services</td>\n",
|
|
" <td>6.244571</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>23</td>\n",
|
|
" <td>28792</td>\n",
|
|
" <td>Construction</td>\n",
|
|
" <td>6.190816</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>42</td>\n",
|
|
" <td>23949</td>\n",
|
|
" <td>Wholesale trade</td>\n",
|
|
" <td>5.149481</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>52</td>\n",
|
|
" <td>17692</td>\n",
|
|
" <td>Finance and insurance</td>\n",
|
|
" <td>3.804109</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>56</td>\n",
|
|
" <td>16769</td>\n",
|
|
" <td>Administrative and support and waste managemen...</td>\n",
|
|
" <td>3.605647</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>31-33</td>\n",
|
|
" <td>13377</td>\n",
|
|
" <td>Manufacturing</td>\n",
|
|
" <td>2.876304</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>53</td>\n",
|
|
" <td>11674</td>\n",
|
|
" <td>Real estate and rental and leasing</td>\n",
|
|
" <td>2.510127</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>48-49</td>\n",
|
|
" <td>10858</td>\n",
|
|
" <td>Transportation and warehousing</td>\n",
|
|
" <td>2.334672</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>71</td>\n",
|
|
" <td>10152</td>\n",
|
|
" <td>Arts, entertainment, and recreation</td>\n",
|
|
" <td>2.182869</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>61</td>\n",
|
|
" <td>5720</td>\n",
|
|
" <td>Educational services</td>\n",
|
|
" <td>1.229907</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>51</td>\n",
|
|
" <td>5645</td>\n",
|
|
" <td>Information</td>\n",
|
|
" <td>1.213780</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>92</td>\n",
|
|
" <td>5047</td>\n",
|
|
" <td>Government Institutions (local, state, and fed...</td>\n",
|
|
" <td>1.085199</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>55</td>\n",
|
|
" <td>2356</td>\n",
|
|
" <td>Management of companies and enterprises</td>\n",
|
|
" <td>0.506584</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>22</td>\n",
|
|
" <td>917</td>\n",
|
|
" <td>Utilities</td>\n",
|
|
" <td>0.197172</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>21</td>\n",
|
|
" <td>832</td>\n",
|
|
" <td>Mining, quarrying, and oil and gas extraction</td>\n",
|
|
" <td>0.178895</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>Missing NAICS value</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" NAICS_CODE ESTAB NAICS2022_LABEL \\\n",
|
|
"14 62 78750 Health care and social assistance \n",
|
|
"10 54 60724 Professional, scientific, and technical services \n",
|
|
"17 81 53562 Other services (except public administration) \n",
|
|
"19 11 49053 Farm Operations \n",
|
|
"5 44-45 40165 Retail trade \n",
|
|
"16 72 29042 Accommodation and food services \n",
|
|
"2 23 28792 Construction \n",
|
|
"4 42 23949 Wholesale trade \n",
|
|
"8 52 17692 Finance and insurance \n",
|
|
"12 56 16769 Administrative and support and waste managemen... \n",
|
|
"3 31-33 13377 Manufacturing \n",
|
|
"9 53 11674 Real estate and rental and leasing \n",
|
|
"6 48-49 10858 Transportation and warehousing \n",
|
|
"15 71 10152 Arts, entertainment, and recreation \n",
|
|
"13 61 5720 Educational services \n",
|
|
"7 51 5645 Information \n",
|
|
"18 92 5047 Government Institutions (local, state, and fed... \n",
|
|
"11 55 2356 Management of companies and enterprises \n",
|
|
"1 22 917 Utilities \n",
|
|
"0 21 832 Mining, quarrying, and oil and gas extraction \n",
|
|
"20 0 0 Missing NAICS value \n",
|
|
"\n",
|
|
" Percentage \n",
|
|
"14 16.932716 \n",
|
|
"10 13.056791 \n",
|
|
"17 11.516827 \n",
|
|
"19 10.547308 \n",
|
|
"5 8.636223 \n",
|
|
"16 6.244571 \n",
|
|
"2 6.190816 \n",
|
|
"4 5.149481 \n",
|
|
"8 3.804109 \n",
|
|
"12 3.605647 \n",
|
|
"3 2.876304 \n",
|
|
"9 2.510127 \n",
|
|
"6 2.334672 \n",
|
|
"15 2.182869 \n",
|
|
"13 1.229907 \n",
|
|
"7 1.213780 \n",
|
|
"18 1.085199 \n",
|
|
"11 0.506584 \n",
|
|
"1 0.197172 \n",
|
|
"0 0.178895 \n",
|
|
"20 0.000000 "
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"naics_df[\"Percentage\"] = (naics_df[\"ESTAB\"] / total_establishments) * 100\n",
|
|
"#naics_df = naics_df[naics_df['NAICS2022_LABEL'].notna() & (naics_df['NAICS2022_LABEL'] != '')]\n",
|
|
"naics_df = naics_df.sort_values(by=['Percentage'], ascending=False)\n",
|
|
"naics_df.head(30)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "218de75d-65c7-46e0-888b-27af0bcc6f80",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Displaying NAICS source data\n",
|
|
"---\n",
|
|
"Now we need a way to show this data in the final report, lets make a table of it. For this I'll be using plotly as matplotlib does not have the needed utilities for making good tables"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "9f263ae7-11c9-47b8-900e-844a54fffe38",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.plotly.v1+json": {
|
|
"config": {
|
|
"plotlyServerURL": "https://plot.ly"
|
|
},
|
|
"data": [
|
|
{
|
|
"cells": {
|
|
"align": [
|
|
"left",
|
|
"left",
|
|
"right"
|
|
],
|
|
"fill": {
|
|
"color": "#bad4d6"
|
|
},
|
|
"font": {
|
|
"color": "black",
|
|
"size": 11
|
|
},
|
|
"values": [
|
|
[
|
|
"62",
|
|
"54",
|
|
"81",
|
|
"11",
|
|
"44-45",
|
|
"72",
|
|
"23",
|
|
"42",
|
|
"52",
|
|
"56",
|
|
"31-33",
|
|
"53",
|
|
"48-49",
|
|
"71",
|
|
"61",
|
|
"51",
|
|
"92",
|
|
"55",
|
|
"22",
|
|
"21",
|
|
"0"
|
|
],
|
|
[
|
|
"Health care and social assistance",
|
|
"Professional, scientific, and technical<br>services",
|
|
"Other services (except public administration)",
|
|
"Farm Operations",
|
|
"Retail trade",
|
|
"Accommodation and food services",
|
|
"Construction",
|
|
"Wholesale trade",
|
|
"Finance and insurance",
|
|
"Administrative and support and waste<br>management and remediation services",
|
|
"Manufacturing",
|
|
"Real estate and rental and leasing",
|
|
"Transportation and warehousing",
|
|
"Arts, entertainment, and recreation",
|
|
"Educational services",
|
|
"Information",
|
|
"Government Institutions (local, state, and<br>federal)",
|
|
"Management of companies and enterprises",
|
|
"Utilities",
|
|
"Mining, quarrying, and oil and gas extraction",
|
|
"Missing NAICS value"
|
|
],
|
|
[
|
|
"16.93%",
|
|
"13.06%",
|
|
"11.52%",
|
|
"10.55%",
|
|
"8.64%",
|
|
"6.24%",
|
|
"6.19%",
|
|
"5.15%",
|
|
"3.80%",
|
|
"3.61%",
|
|
"2.88%",
|
|
"2.51%",
|
|
"2.33%",
|
|
"2.18%",
|
|
"1.23%",
|
|
"1.21%",
|
|
"1.09%",
|
|
"0.51%",
|
|
"0.20%",
|
|
"0.18%",
|
|
"0.00%"
|
|
]
|
|
]
|
|
},
|
|
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|
|
0.15,
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0.5,
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0.15
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|
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|
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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"table_headers = [\"Sector\", \"Definition\", \"PA Census Percentage\"]\n",
|
|
"\n",
|
|
"# For whatever reason, there are 2 of these identical columns\n",
|
|
"naics_codes = naics_df[\"NAICS_CODE\"]\n",
|
|
"naics_code_names = naics_df[\"NAICS2022_LABEL\"]\n",
|
|
"percentages = [f\"{x:.2f}%\" for x in naics_df[\"Percentage\"]]\n",
|
|
"\n",
|
|
"# --- 2. Create a new list of \"wrapped\" names ---\n",
|
|
"# This loops through each name, wraps it at 45 characters,\n",
|
|
"# and joins the lines with an HTML break <br>\n",
|
|
"width = 45 # Adjust this width as needed\n",
|
|
"wrapped_names = ['<br>'.join(textwrap.wrap(name, width=width)) for name in naics_code_names]\n",
|
|
"\n",
|
|
"\n",
|
|
"fig = go.Figure(data=[\n",
|
|
" go.Table(\n",
|
|
" header=dict(values=table_headers, fill_color='#6ebe4d'),\n",
|
|
" cells=dict(\n",
|
|
" # --- 3. Use the new 'wrapped_names' list ---\n",
|
|
" values=[naics_codes, wrapped_names, percentages],\n",
|
|
" \n",
|
|
" # --- 4. REMOVE the height=100 parameter ---\n",
|
|
" # This lets Plotly auto-size the row height to fit the wrapped text.\n",
|
|
" \n",
|
|
" # Optional: Align all text to the left/top for readability\n",
|
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" align=['left', 'left', 'right'],\n",
|
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" font=dict(size=11, color='black'),\n",
|
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" fill_color='#bad4d6'\n",
|
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" ),\n",
|
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" columnwidth=[0.15, 0.5, 0.15], # Your column widths are good\n",
|
|
" )]\n",
|
|
")\n",
|
|
"\n",
|
|
"fig.update_layout(\n",
|
|
" height=575,\n",
|
|
" width=500,\n",
|
|
" margin=dict(l=10, r=10, t=10, b=10)\n",
|
|
")\n",
|
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" \n",
|
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"\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"fig.write_image(\"sector_table.jpg\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c6f73aa0-a9fc-4b78-8829-d252b24a745e",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Cleaning Up the Neoserra Data\n",
|
|
"---\n",
|
|
"Now that we have the census data cleaned up and in a usable form, we need to merge it with the neoserra data in a graphic that compares the NAICS codes of the clients served.\n",
|
|
"\n",
|
|
"The data for this section can be obtained by running the following filter and exporting a quick report: https://pasbdc.neoserra.com/clients?__formid=3&remove=&savename=&sort=CLIENT_ID&sortdir=ASC&expr=&field_1=REVIEWID&opt_1=13213656&field_2=&sortdir=ASC\n",
|
|
"\n",
|
|
"OR by going to this goalcard and following through to the clients served: https://pasbdc.neoserra.com/scorecards/goalset/708"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "e2bd80bd-db49-4e8d-89d3-2d6b2b2aa85e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
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"11050 Clients Total\n"
|
|
]
|
|
},
|
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{
|
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"data": {
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"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Client ID</th>\n",
|
|
" <th>Client</th>\n",
|
|
" <th>Last Counseling</th>\n",
|
|
" <th>Center</th>\n",
|
|
" <th>Physical Address County</th>\n",
|
|
" <th>NAICs</th>\n",
|
|
" <th>Primary NAICS</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>WD04170</td>\n",
|
|
" <td>\\tProinnov@ LLC (WD04170)</td>\n",
|
|
" <td>9/9/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Philadelphia</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>WD02759</td>\n",
|
|
" <td>\"C.J.A.\"/ Crawley Jones and Allen real estate...</td>\n",
|
|
" <td>10/20/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Delaware</td>\n",
|
|
" <td>531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\...</td>\n",
|
|
" <td>531390 - Other Activities Related to Real Esta...</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>PS018402</td>\n",
|
|
" <td>Anjie's Cleaning Bees (PS018402)</td>\n",
|
|
" <td>10/14/2024 12:00 AM</td>\n",
|
|
" <td>Penn State SBDC</td>\n",
|
|
" <td>Lycoming</td>\n",
|
|
" <td>561720-JanitorialServices\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>561720 - Janitorial Services \\r\\r\\n</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>C8538</td>\n",
|
|
" <td>BRENIMAN PROPERTIES, LLC (C8538)</td>\n",
|
|
" <td>10/17/2025 12:00 AM</td>\n",
|
|
" <td>PennWest University Clarion SBDC</td>\n",
|
|
" <td>Clarion</td>\n",
|
|
" <td>531120-LessorsofNonresidentialBuildings(except...</td>\n",
|
|
" <td>531120 - Lessors of Nonresidential Buildings (...</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>BU016079</td>\n",
|
|
" <td>Civil War Cider Co., Inc. (BU016079)</td>\n",
|
|
" <td>10/21/2024 12:00 AM</td>\n",
|
|
" <td>Bucknell SBDC</td>\n",
|
|
" <td>Union</td>\n",
|
|
" <td>312130-Wineries\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>312130 - Wineries \\r\\r\\n</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Client ID Client \\\n",
|
|
"0 WD04170 \\tProinnov@ LLC (WD04170) \n",
|
|
"1 WD02759 \"C.J.A.\"/ Crawley Jones and Allen real estate... \n",
|
|
"2 PS018402 Anjie's Cleaning Bees (PS018402) \n",
|
|
"3 C8538 BRENIMAN PROPERTIES, LLC (C8538) \n",
|
|
"4 BU016079 Civil War Cider Co., Inc. (BU016079) \n",
|
|
"\n",
|
|
" Last Counseling Center \\\n",
|
|
"0 9/9/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"1 10/20/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"2 10/14/2024 12:00 AM Penn State SBDC \n",
|
|
"3 10/17/2025 12:00 AM PennWest University Clarion SBDC \n",
|
|
"4 10/21/2024 12:00 AM Bucknell SBDC \n",
|
|
"\n",
|
|
" Physical Address County NAICs \\\n",
|
|
"0 Philadelphia NaN \n",
|
|
"1 Delaware 531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\... \n",
|
|
"2 Lycoming 561720-JanitorialServices\\r\\r\\n\\r\\r\\n \n",
|
|
"3 Clarion 531120-LessorsofNonresidentialBuildings(except... \n",
|
|
"4 Union 312130-Wineries\\r\\r\\n\\r\\r\\n \n",
|
|
"\n",
|
|
" Primary NAICS \n",
|
|
"0 NaN \n",
|
|
"1 531390 - Other Activities Related to Real Esta... \n",
|
|
"2 561720 - Janitorial Services \\r\\r\\n \n",
|
|
"3 531120 - Lessors of Nonresidential Buildings (... \n",
|
|
"4 312130 - Wineries \\r\\r\\n "
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"client_naics_df = pd.read_csv(\"clients_naics_data.csv\")\n",
|
|
"client_count = client_naics_df.shape[0]\n",
|
|
"print(client_count,\"Clients Total\")\n",
|
|
"client_naics_df.head()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "539b75ad-c032-478e-9d35-514eecbe1ed4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"missing_naics_df = client_naics_df[client_naics_df[\"NAICs\"].isna() & client_naics_df[\"Primary NAICS\"].isna()]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "e3d34a69-5f0a-4a68-9da3-2d202cdbbe77",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1526 Clients with missing NAICS values\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
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|
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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|
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" }\n",
|
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"\n",
|
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" .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>Client ID</th>\n",
|
|
" <th>Client</th>\n",
|
|
" <th>Last Counseling</th>\n",
|
|
" <th>Center</th>\n",
|
|
" <th>Physical Address County</th>\n",
|
|
" <th>NAICs</th>\n",
|
|
" <th>Primary NAICS</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>WD04170</td>\n",
|
|
" <td>\\tProinnov@ LLC (WD04170)</td>\n",
|
|
" <td>9/9/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Philadelphia</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>WD04124</td>\n",
|
|
" <td>Diaz Flat Fix La guinea (WD04124)</td>\n",
|
|
" <td>9/9/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Philadelphia</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>23</th>\n",
|
|
" <td>BU018452</td>\n",
|
|
" <td>\"Nick\" Martin Shelbo (BU018452)</td>\n",
|
|
" <td>7/3/2025 12:00 AM</td>\n",
|
|
" <td>Bucknell SBDC</td>\n",
|
|
" <td>Northumberland</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>27</th>\n",
|
|
" <td>KUP269380</td>\n",
|
|
" <td>????? ?????????? (KUP269380)</td>\n",
|
|
" <td>2/14/2025 12:00 AM</td>\n",
|
|
" <td>Kutztown University SBDC</td>\n",
|
|
" <td>Chester</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28</th>\n",
|
|
" <td>025327</td>\n",
|
|
" <td>@companion carers Inc (025327)</td>\n",
|
|
" <td>9/29/2025 12:00 AM</td>\n",
|
|
" <td>LE - LEHIGH UNIVERSITY SBDC</td>\n",
|
|
" <td>Bucks</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Client ID Client Last Counseling \\\n",
|
|
"0 WD04170 \\tProinnov@ LLC (WD04170) 9/9/2025 12:00 AM \n",
|
|
"6 WD04124 Diaz Flat Fix La guinea (WD04124) 9/9/2025 12:00 AM \n",
|
|
"23 BU018452 \"Nick\" Martin Shelbo (BU018452) 7/3/2025 12:00 AM \n",
|
|
"27 KUP269380 ????? ?????????? (KUP269380) 2/14/2025 12:00 AM \n",
|
|
"28 025327 @companion carers Inc (025327) 9/29/2025 12:00 AM \n",
|
|
"\n",
|
|
" Center Physical Address County NAICs Primary NAICS \n",
|
|
"0 WD - WIDENER SBDC Philadelphia NaN NaN \n",
|
|
"6 WD - WIDENER SBDC Philadelphia NaN NaN \n",
|
|
"23 Bucknell SBDC Northumberland NaN NaN \n",
|
|
"27 Kutztown University SBDC Chester NaN NaN \n",
|
|
"28 LE - LEHIGH UNIVERSITY SBDC Bucks NaN NaN "
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"missing_count = missing_naics_df.shape[0]\n",
|
|
"print(missing_count, \"Clients with missing NAICS values\")\n",
|
|
"missing_naics_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "869bddcd-e75d-4ca7-8ba4-abc089f08563",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"13.81% of clients are missing NAICS values\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(f\"{(missing_count / client_count) * 100 :.2f}%\", \"of clients are missing NAICS values\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "adb5a9a8-2b22-40c8-8387-d775040a85d3",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Combining the data\n",
|
|
"---\n",
|
|
"Now we need to take the census data we downloaded before, and tag each client with the assocaited percentage.\n",
|
|
"\n",
|
|
"The first step of this is to turn the 6 digit code into a 2 digit identifier. To do this we just take the first 2 digits.\n",
|
|
"\n",
|
|
"We also need to process the census data a little bit more because of the ranges of codes used within the counts."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"id": "cc6327a9-8c6d-4634-85d1-c1043d9dcaab",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
|
|
"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
|
" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Client ID</th>\n",
|
|
" <th>Client</th>\n",
|
|
" <th>Last Counseling</th>\n",
|
|
" <th>Center</th>\n",
|
|
" <th>Physical Address County</th>\n",
|
|
" <th>NAICs</th>\n",
|
|
" <th>Primary NAICS</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>WD04170</td>\n",
|
|
" <td>\\tProinnov@ LLC (WD04170)</td>\n",
|
|
" <td>9/9/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Philadelphia</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>WD02759</td>\n",
|
|
" <td>\"C.J.A.\"/ Crawley Jones and Allen real estate...</td>\n",
|
|
" <td>10/20/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Delaware</td>\n",
|
|
" <td>531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\...</td>\n",
|
|
" <td>531390 - Other Activities Related to Real Esta...</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>PS018402</td>\n",
|
|
" <td>Anjie's Cleaning Bees (PS018402)</td>\n",
|
|
" <td>10/14/2024 12:00 AM</td>\n",
|
|
" <td>Penn State SBDC</td>\n",
|
|
" <td>Lycoming</td>\n",
|
|
" <td>561720-JanitorialServices\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>561720 - Janitorial Services \\r\\r\\n</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>C8538</td>\n",
|
|
" <td>BRENIMAN PROPERTIES, LLC (C8538)</td>\n",
|
|
" <td>10/17/2025 12:00 AM</td>\n",
|
|
" <td>PennWest University Clarion SBDC</td>\n",
|
|
" <td>Clarion</td>\n",
|
|
" <td>531120-LessorsofNonresidentialBuildings(except...</td>\n",
|
|
" <td>531120 - Lessors of Nonresidential Buildings (...</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>BU016079</td>\n",
|
|
" <td>Civil War Cider Co., Inc. (BU016079)</td>\n",
|
|
" <td>10/21/2024 12:00 AM</td>\n",
|
|
" <td>Bucknell SBDC</td>\n",
|
|
" <td>Union</td>\n",
|
|
" <td>312130-Wineries\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>312130 - Wineries \\r\\r\\n</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Client ID Client \\\n",
|
|
"0 WD04170 \\tProinnov@ LLC (WD04170) \n",
|
|
"1 WD02759 \"C.J.A.\"/ Crawley Jones and Allen real estate... \n",
|
|
"2 PS018402 Anjie's Cleaning Bees (PS018402) \n",
|
|
"3 C8538 BRENIMAN PROPERTIES, LLC (C8538) \n",
|
|
"4 BU016079 Civil War Cider Co., Inc. (BU016079) \n",
|
|
"\n",
|
|
" Last Counseling Center \\\n",
|
|
"0 9/9/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"1 10/20/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"2 10/14/2024 12:00 AM Penn State SBDC \n",
|
|
"3 10/17/2025 12:00 AM PennWest University Clarion SBDC \n",
|
|
"4 10/21/2024 12:00 AM Bucknell SBDC \n",
|
|
"\n",
|
|
" Physical Address County NAICs \\\n",
|
|
"0 Philadelphia NaN \n",
|
|
"1 Delaware 531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\... \n",
|
|
"2 Lycoming 561720-JanitorialServices\\r\\r\\n\\r\\r\\n \n",
|
|
"3 Clarion 531120-LessorsofNonresidentialBuildings(except... \n",
|
|
"4 Union 312130-Wineries\\r\\r\\n\\r\\r\\n \n",
|
|
"\n",
|
|
" Primary NAICS \n",
|
|
"0 NaN \n",
|
|
"1 531390 - Other Activities Related to Real Esta... \n",
|
|
"2 561720 - Janitorial Services \\r\\r\\n \n",
|
|
"3 531120 - Lessors of Nonresidential Buildings (... \n",
|
|
"4 312130 - Wineries \\r\\r\\n "
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"client_naics_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "415205d4-1ad3-4b78-9a05-925f29deb42f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"def tag_two_digit_naics(row):\n",
|
|
" new_col_name = \"NAICS_2\"\n",
|
|
"\n",
|
|
" if pd.isna(row[\"NAICs\"]) and pd.isna(row[\"Primary NAICS\"]):\n",
|
|
" row[new_col_name] = np.nan; \n",
|
|
" return row\n",
|
|
"\n",
|
|
" if pd.isna(row[\"NAICs\"]):\n",
|
|
" row[new_col_name] = int(row[\"Primary NAICS\"][:2])\n",
|
|
" return row\n",
|
|
" elif pd.isna(row[\"Primary NAICS\"]):\n",
|
|
" row[new_col_name] = int(row[\"NAICs\"][:2])\n",
|
|
" return row\n",
|
|
" else:\n",
|
|
" # Then both columns are not na, default to the Primary NAICs\n",
|
|
" row[new_col_name] = int(row[\"Primary NAICS\"][:2])\n",
|
|
" return row\n",
|
|
" \n",
|
|
" return row \n",
|
|
" \n",
|
|
"client_naics_df = client_naics_df.apply(tag_two_digit_naics, axis=1)\n",
|
|
"\n",
|
|
"# Deal with missing NAICS values\n",
|
|
"client_naics_df[\"NAICS_2\"] = client_naics_df[\"NAICS_2\"].fillna(0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "b52f9656-3d61-4e81-9fb9-de9c0ab3ffce",
|
|
"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>NAICS_CODE</th>\n",
|
|
" <th>ESTAB</th>\n",
|
|
" <th>NAICS2022_LABEL</th>\n",
|
|
" <th>Percentage</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>62</td>\n",
|
|
" <td>78750</td>\n",
|
|
" <td>Health care and social assistance</td>\n",
|
|
" <td>16.932716</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>54</td>\n",
|
|
" <td>60724</td>\n",
|
|
" <td>Professional, scientific, and technical services</td>\n",
|
|
" <td>13.056791</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>81</td>\n",
|
|
" <td>53562</td>\n",
|
|
" <td>Other services (except public administration)</td>\n",
|
|
" <td>11.516827</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>11</td>\n",
|
|
" <td>49053</td>\n",
|
|
" <td>Farm Operations</td>\n",
|
|
" <td>10.547308</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>44-45</td>\n",
|
|
" <td>40165</td>\n",
|
|
" <td>Retail trade</td>\n",
|
|
" <td>8.636223</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>72</td>\n",
|
|
" <td>29042</td>\n",
|
|
" <td>Accommodation and food services</td>\n",
|
|
" <td>6.244571</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>23</td>\n",
|
|
" <td>28792</td>\n",
|
|
" <td>Construction</td>\n",
|
|
" <td>6.190816</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>42</td>\n",
|
|
" <td>23949</td>\n",
|
|
" <td>Wholesale trade</td>\n",
|
|
" <td>5.149481</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>52</td>\n",
|
|
" <td>17692</td>\n",
|
|
" <td>Finance and insurance</td>\n",
|
|
" <td>3.804109</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>56</td>\n",
|
|
" <td>16769</td>\n",
|
|
" <td>Administrative and support and waste managemen...</td>\n",
|
|
" <td>3.605647</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>31-33</td>\n",
|
|
" <td>13377</td>\n",
|
|
" <td>Manufacturing</td>\n",
|
|
" <td>2.876304</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>53</td>\n",
|
|
" <td>11674</td>\n",
|
|
" <td>Real estate and rental and leasing</td>\n",
|
|
" <td>2.510127</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>48-49</td>\n",
|
|
" <td>10858</td>\n",
|
|
" <td>Transportation and warehousing</td>\n",
|
|
" <td>2.334672</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>71</td>\n",
|
|
" <td>10152</td>\n",
|
|
" <td>Arts, entertainment, and recreation</td>\n",
|
|
" <td>2.182869</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>61</td>\n",
|
|
" <td>5720</td>\n",
|
|
" <td>Educational services</td>\n",
|
|
" <td>1.229907</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>51</td>\n",
|
|
" <td>5645</td>\n",
|
|
" <td>Information</td>\n",
|
|
" <td>1.213780</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>92</td>\n",
|
|
" <td>5047</td>\n",
|
|
" <td>Government Institutions (local, state, and fed...</td>\n",
|
|
" <td>1.085199</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>55</td>\n",
|
|
" <td>2356</td>\n",
|
|
" <td>Management of companies and enterprises</td>\n",
|
|
" <td>0.506584</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>22</td>\n",
|
|
" <td>917</td>\n",
|
|
" <td>Utilities</td>\n",
|
|
" <td>0.197172</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>21</td>\n",
|
|
" <td>832</td>\n",
|
|
" <td>Mining, quarrying, and oil and gas extraction</td>\n",
|
|
" <td>0.178895</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>Missing NAICS value</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" NAICS_CODE ESTAB NAICS2022_LABEL \\\n",
|
|
"14 62 78750 Health care and social assistance \n",
|
|
"10 54 60724 Professional, scientific, and technical services \n",
|
|
"17 81 53562 Other services (except public administration) \n",
|
|
"19 11 49053 Farm Operations \n",
|
|
"5 44-45 40165 Retail trade \n",
|
|
"16 72 29042 Accommodation and food services \n",
|
|
"2 23 28792 Construction \n",
|
|
"4 42 23949 Wholesale trade \n",
|
|
"8 52 17692 Finance and insurance \n",
|
|
"12 56 16769 Administrative and support and waste managemen... \n",
|
|
"3 31-33 13377 Manufacturing \n",
|
|
"9 53 11674 Real estate and rental and leasing \n",
|
|
"6 48-49 10858 Transportation and warehousing \n",
|
|
"15 71 10152 Arts, entertainment, and recreation \n",
|
|
"13 61 5720 Educational services \n",
|
|
"7 51 5645 Information \n",
|
|
"18 92 5047 Government Institutions (local, state, and fed... \n",
|
|
"11 55 2356 Management of companies and enterprises \n",
|
|
"1 22 917 Utilities \n",
|
|
"0 21 832 Mining, quarrying, and oil and gas extraction \n",
|
|
"20 0 0 Missing NAICS value \n",
|
|
"\n",
|
|
" Percentage \n",
|
|
"14 16.932716 \n",
|
|
"10 13.056791 \n",
|
|
"17 11.516827 \n",
|
|
"19 10.547308 \n",
|
|
"5 8.636223 \n",
|
|
"16 6.244571 \n",
|
|
"2 6.190816 \n",
|
|
"4 5.149481 \n",
|
|
"8 3.804109 \n",
|
|
"12 3.605647 \n",
|
|
"3 2.876304 \n",
|
|
"9 2.510127 \n",
|
|
"6 2.334672 \n",
|
|
"15 2.182869 \n",
|
|
"13 1.229907 \n",
|
|
"7 1.213780 \n",
|
|
"18 1.085199 \n",
|
|
"11 0.506584 \n",
|
|
"1 0.197172 \n",
|
|
"0 0.178895 \n",
|
|
"20 0.000000 "
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"naics_df.head(30)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "014f9a41-6f5f-4ea0-8768-568097fc1498",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"62 = 16.932716373237923\n",
|
|
"54 = 13.056790718076186\n",
|
|
"81 = 11.516827357249138\n",
|
|
"11 = 10.547308396907171\n",
|
|
"44 = 8.636222896902872\n",
|
|
"45 = 8.636222896902872\n",
|
|
"72 = 6.244570779829534\n",
|
|
"23 = 6.190816124676397\n",
|
|
"42 = 5.1494809450498416\n",
|
|
"52 = 3.804109435877147\n",
|
|
"56 = 3.605647249051768\n",
|
|
"31 = 2.876304087934015\n",
|
|
"32 = 2.876304087934015\n",
|
|
"33 = 2.876304087934015\n",
|
|
"53 = 2.5101273770308508\n",
|
|
"48 = 2.3346721826110137\n",
|
|
"49 = 2.3346721826110137\n",
|
|
"71 = 2.1828690364585577\n",
|
|
"61 = 1.2299065099037576\n",
|
|
"51 = 1.2137801133578168\n",
|
|
"92 = 1.0851989782315148\n",
|
|
"55 = 0.5065838701631562\n",
|
|
"22 = 0.19717207510170381\n",
|
|
"21 = 0.17889549234963747\n",
|
|
"0 = 0.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create a mapping between the 2 didgit NAICS code and the assocaited percentage in PA\n",
|
|
"# 0 will represent no data\n",
|
|
"naics_mapping = {0: 0.0}\n",
|
|
"\n",
|
|
"for index, row in naics_df.iterrows():\n",
|
|
" split = str(row[\"NAICS_CODE\"]).split('-')\n",
|
|
" if len(split) == 2:\n",
|
|
" for code in range(int(split[0]), int(split[1]) + 1):\n",
|
|
" naics_mapping[code] = float(row[\"Percentage\"])\n",
|
|
" print(code, \"=\", row[\"Percentage\"])\n",
|
|
" else:\n",
|
|
" naics_mapping[int(row[\"NAICS_CODE\"])] = float(row[\"Percentage\"])\n",
|
|
" print(int(row[\"NAICS_CODE\"]), \"=\", row[\"Percentage\"])\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"id": "fa3fb374-ef70-4781-8e29-9a535e66e824",
|
|
"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>Client ID</th>\n",
|
|
" <th>Client</th>\n",
|
|
" <th>Last Counseling</th>\n",
|
|
" <th>Center</th>\n",
|
|
" <th>Physical Address County</th>\n",
|
|
" <th>NAICs</th>\n",
|
|
" <th>Primary NAICS</th>\n",
|
|
" <th>NAICS_2</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>WD04170</td>\n",
|
|
" <td>\\tProinnov@ LLC (WD04170)</td>\n",
|
|
" <td>9/9/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Philadelphia</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>WD02759</td>\n",
|
|
" <td>\"C.J.A.\"/ Crawley Jones and Allen real estate...</td>\n",
|
|
" <td>10/20/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Delaware</td>\n",
|
|
" <td>531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\...</td>\n",
|
|
" <td>531390 - Other Activities Related to Real Esta...</td>\n",
|
|
" <td>53.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>PS018402</td>\n",
|
|
" <td>Anjie's Cleaning Bees (PS018402)</td>\n",
|
|
" <td>10/14/2024 12:00 AM</td>\n",
|
|
" <td>Penn State SBDC</td>\n",
|
|
" <td>Lycoming</td>\n",
|
|
" <td>561720-JanitorialServices\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>561720 - Janitorial Services \\r\\r\\n</td>\n",
|
|
" <td>56.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>C8538</td>\n",
|
|
" <td>BRENIMAN PROPERTIES, LLC (C8538)</td>\n",
|
|
" <td>10/17/2025 12:00 AM</td>\n",
|
|
" <td>PennWest University Clarion SBDC</td>\n",
|
|
" <td>Clarion</td>\n",
|
|
" <td>531120-LessorsofNonresidentialBuildings(except...</td>\n",
|
|
" <td>531120 - Lessors of Nonresidential Buildings (...</td>\n",
|
|
" <td>53.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>BU016079</td>\n",
|
|
" <td>Civil War Cider Co., Inc. (BU016079)</td>\n",
|
|
" <td>10/21/2024 12:00 AM</td>\n",
|
|
" <td>Bucknell SBDC</td>\n",
|
|
" <td>Union</td>\n",
|
|
" <td>312130-Wineries\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>312130 - Wineries \\r\\r\\n</td>\n",
|
|
" <td>31.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Client ID Client \\\n",
|
|
"0 WD04170 \\tProinnov@ LLC (WD04170) \n",
|
|
"1 WD02759 \"C.J.A.\"/ Crawley Jones and Allen real estate... \n",
|
|
"2 PS018402 Anjie's Cleaning Bees (PS018402) \n",
|
|
"3 C8538 BRENIMAN PROPERTIES, LLC (C8538) \n",
|
|
"4 BU016079 Civil War Cider Co., Inc. (BU016079) \n",
|
|
"\n",
|
|
" Last Counseling Center \\\n",
|
|
"0 9/9/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"1 10/20/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"2 10/14/2024 12:00 AM Penn State SBDC \n",
|
|
"3 10/17/2025 12:00 AM PennWest University Clarion SBDC \n",
|
|
"4 10/21/2024 12:00 AM Bucknell SBDC \n",
|
|
"\n",
|
|
" Physical Address County NAICs \\\n",
|
|
"0 Philadelphia NaN \n",
|
|
"1 Delaware 531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\... \n",
|
|
"2 Lycoming 561720-JanitorialServices\\r\\r\\n\\r\\r\\n \n",
|
|
"3 Clarion 531120-LessorsofNonresidentialBuildings(except... \n",
|
|
"4 Union 312130-Wineries\\r\\r\\n\\r\\r\\n \n",
|
|
"\n",
|
|
" Primary NAICS NAICS_2 \n",
|
|
"0 NaN 0.0 \n",
|
|
"1 531390 - Other Activities Related to Real Esta... 53.0 \n",
|
|
"2 561720 - Janitorial Services \\r\\r\\n 56.0 \n",
|
|
"3 531120 - Lessors of Nonresidential Buildings (... 53.0 \n",
|
|
"4 312130 - Wineries \\r\\r\\n 31.0 "
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"client_naics_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "66f3e784-f1d3-4c05-91a4-36644eff629f",
|
|
"metadata": {},
|
|
"outputs": [
|
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{
|
|
"data": {
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"text/html": [
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"<style scoped>\n",
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"</style>\n",
|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Client ID</th>\n",
|
|
" <th>Client</th>\n",
|
|
" <th>Last Counseling</th>\n",
|
|
" <th>Center</th>\n",
|
|
" <th>Physical Address County</th>\n",
|
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" <th>NAICs</th>\n",
|
|
" <th>Primary NAICS</th>\n",
|
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" <th>NAICS_2</th>\n",
|
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" <th>PA NAICs Code Percentage</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>WD04170</td>\n",
|
|
" <td>\\tProinnov@ LLC (WD04170)</td>\n",
|
|
" <td>9/9/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Philadelphia</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>WD02759</td>\n",
|
|
" <td>\"C.J.A.\"/ Crawley Jones and Allen real estate...</td>\n",
|
|
" <td>10/20/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Delaware</td>\n",
|
|
" <td>531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\...</td>\n",
|
|
" <td>531390 - Other Activities Related to Real Esta...</td>\n",
|
|
" <td>53.0</td>\n",
|
|
" <td>2.510127</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>PS018402</td>\n",
|
|
" <td>Anjie's Cleaning Bees (PS018402)</td>\n",
|
|
" <td>10/14/2024 12:00 AM</td>\n",
|
|
" <td>Penn State SBDC</td>\n",
|
|
" <td>Lycoming</td>\n",
|
|
" <td>561720-JanitorialServices\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>561720 - Janitorial Services \\r\\r\\n</td>\n",
|
|
" <td>56.0</td>\n",
|
|
" <td>3.605647</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>C8538</td>\n",
|
|
" <td>BRENIMAN PROPERTIES, LLC (C8538)</td>\n",
|
|
" <td>10/17/2025 12:00 AM</td>\n",
|
|
" <td>PennWest University Clarion SBDC</td>\n",
|
|
" <td>Clarion</td>\n",
|
|
" <td>531120-LessorsofNonresidentialBuildings(except...</td>\n",
|
|
" <td>531120 - Lessors of Nonresidential Buildings (...</td>\n",
|
|
" <td>53.0</td>\n",
|
|
" <td>2.510127</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>BU016079</td>\n",
|
|
" <td>Civil War Cider Co., Inc. (BU016079)</td>\n",
|
|
" <td>10/21/2024 12:00 AM</td>\n",
|
|
" <td>Bucknell SBDC</td>\n",
|
|
" <td>Union</td>\n",
|
|
" <td>312130-Wineries\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>312130 - Wineries \\r\\r\\n</td>\n",
|
|
" <td>31.0</td>\n",
|
|
" <td>2.876304</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Client ID Client \\\n",
|
|
"0 WD04170 \\tProinnov@ LLC (WD04170) \n",
|
|
"1 WD02759 \"C.J.A.\"/ Crawley Jones and Allen real estate... \n",
|
|
"2 PS018402 Anjie's Cleaning Bees (PS018402) \n",
|
|
"3 C8538 BRENIMAN PROPERTIES, LLC (C8538) \n",
|
|
"4 BU016079 Civil War Cider Co., Inc. (BU016079) \n",
|
|
"\n",
|
|
" Last Counseling Center \\\n",
|
|
"0 9/9/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"1 10/20/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"2 10/14/2024 12:00 AM Penn State SBDC \n",
|
|
"3 10/17/2025 12:00 AM PennWest University Clarion SBDC \n",
|
|
"4 10/21/2024 12:00 AM Bucknell SBDC \n",
|
|
"\n",
|
|
" Physical Address County NAICs \\\n",
|
|
"0 Philadelphia NaN \n",
|
|
"1 Delaware 531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\... \n",
|
|
"2 Lycoming 561720-JanitorialServices\\r\\r\\n\\r\\r\\n \n",
|
|
"3 Clarion 531120-LessorsofNonresidentialBuildings(except... \n",
|
|
"4 Union 312130-Wineries\\r\\r\\n\\r\\r\\n \n",
|
|
"\n",
|
|
" Primary NAICS NAICS_2 \\\n",
|
|
"0 NaN 0.0 \n",
|
|
"1 531390 - Other Activities Related to Real Esta... 53.0 \n",
|
|
"2 561720 - Janitorial Services \\r\\r\\n 56.0 \n",
|
|
"3 531120 - Lessors of Nonresidential Buildings (... 53.0 \n",
|
|
"4 312130 - Wineries \\r\\r\\n 31.0 \n",
|
|
"\n",
|
|
" PA NAICs Code Percentage \n",
|
|
"0 0.000000 \n",
|
|
"1 2.510127 \n",
|
|
"2 3.605647 \n",
|
|
"3 2.510127 \n",
|
|
"4 2.876304 "
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Modify the datframe and tag it\n",
|
|
"\n",
|
|
"def tag_naics_percentatge(df):\n",
|
|
" try:\n",
|
|
" df[\"PA NAICs Code Percentage\"] = naics_mapping[int(df[\"NAICS_2\"])]\n",
|
|
" except Exception as e:\n",
|
|
" if not pd.isna(df[\"NAICS_2\"]):\n",
|
|
" if int(df[\"NAICS_2\"]) == 11 or int(df[\"NAICS_2\"]) == 92:\n",
|
|
" return df\n",
|
|
" \n",
|
|
" if not pd.isna(df[\"NAICS_2\"]):\n",
|
|
" print(type(df[\"NAICS_2\"]), df[\"NAICS_2\"], \"did not have an associated percentage\")\n",
|
|
" \n",
|
|
" return df;\n",
|
|
" \n",
|
|
" return df;\n",
|
|
"\n",
|
|
"client_naics_df = client_naics_df.apply(tag_naics_percentatge, axis=1)\n",
|
|
"client_naics_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b13a18c8-7e7b-4c22-8e58-edec177fb8e9",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Tagging PASBDC client percentages\n",
|
|
"---\n",
|
|
"Now that we have calculated the NAICS code percentages in PA based on census data and tagged it to our dataframe, we can calculate the same percentage for clients served within the PASBDC network\n",
|
|
"\n",
|
|
"# NOTE: When I am calculating percentages here, I am not doing it out of the total number of clients, I am doing it out of the total number of clients who have a tagged NAICS code"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"id": "3545ef45-7efc-44d8-bb7e-59cd821799b8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"11050 total clients (with a tagged NAICS code)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"naics_value_counts = client_naics_df['NAICS_2'].value_counts()\n",
|
|
"naics_value_counts.head(30)\n",
|
|
"total_clients = naics_value_counts.sum()\n",
|
|
"print(total_clients, \"total clients (with a tagged NAICS code)\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"id": "cc3d2e52-430d-482f-9abf-b46f642f1986",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
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|
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|
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|
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|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Client ID</th>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <th>Primary NAICS</th>\n",
|
|
" <th>NAICS_2</th>\n",
|
|
" <th>PA NAICs Code Percentage</th>\n",
|
|
" <th>PASBDC NAICs Code Percentage</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>WD04170</td>\n",
|
|
" <td>\\tProinnov@ LLC (WD04170)</td>\n",
|
|
" <td>9/9/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Philadelphia</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>13.809955</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>WD02759</td>\n",
|
|
" <td>\"C.J.A.\"/ Crawley Jones and Allen real estate...</td>\n",
|
|
" <td>10/20/2025 12:00 AM</td>\n",
|
|
" <td>WD - WIDENER SBDC</td>\n",
|
|
" <td>Delaware</td>\n",
|
|
" <td>531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\...</td>\n",
|
|
" <td>531390 - Other Activities Related to Real Esta...</td>\n",
|
|
" <td>53.0</td>\n",
|
|
" <td>2.510127</td>\n",
|
|
" <td>2.723982</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>PS018402</td>\n",
|
|
" <td>Anjie's Cleaning Bees (PS018402)</td>\n",
|
|
" <td>10/14/2024 12:00 AM</td>\n",
|
|
" <td>Penn State SBDC</td>\n",
|
|
" <td>Lycoming</td>\n",
|
|
" <td>561720-JanitorialServices\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>561720 - Janitorial Services \\r\\r\\n</td>\n",
|
|
" <td>56.0</td>\n",
|
|
" <td>3.605647</td>\n",
|
|
" <td>4.398190</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>C8538</td>\n",
|
|
" <td>BRENIMAN PROPERTIES, LLC (C8538)</td>\n",
|
|
" <td>10/17/2025 12:00 AM</td>\n",
|
|
" <td>PennWest University Clarion SBDC</td>\n",
|
|
" <td>Clarion</td>\n",
|
|
" <td>531120-LessorsofNonresidentialBuildings(except...</td>\n",
|
|
" <td>531120 - Lessors of Nonresidential Buildings (...</td>\n",
|
|
" <td>53.0</td>\n",
|
|
" <td>2.510127</td>\n",
|
|
" <td>2.723982</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>BU016079</td>\n",
|
|
" <td>Civil War Cider Co., Inc. (BU016079)</td>\n",
|
|
" <td>10/21/2024 12:00 AM</td>\n",
|
|
" <td>Bucknell SBDC</td>\n",
|
|
" <td>Union</td>\n",
|
|
" <td>312130-Wineries\\r\\r\\n\\r\\r\\n</td>\n",
|
|
" <td>312130 - Wineries \\r\\r\\n</td>\n",
|
|
" <td>31.0</td>\n",
|
|
" <td>2.876304</td>\n",
|
|
" <td>4.995475</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Client ID Client \\\n",
|
|
"0 WD04170 \\tProinnov@ LLC (WD04170) \n",
|
|
"1 WD02759 \"C.J.A.\"/ Crawley Jones and Allen real estate... \n",
|
|
"2 PS018402 Anjie's Cleaning Bees (PS018402) \n",
|
|
"3 C8538 BRENIMAN PROPERTIES, LLC (C8538) \n",
|
|
"4 BU016079 Civil War Cider Co., Inc. (BU016079) \n",
|
|
"\n",
|
|
" Last Counseling Center \\\n",
|
|
"0 9/9/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"1 10/20/2025 12:00 AM WD - WIDENER SBDC \n",
|
|
"2 10/14/2024 12:00 AM Penn State SBDC \n",
|
|
"3 10/17/2025 12:00 AM PennWest University Clarion SBDC \n",
|
|
"4 10/21/2024 12:00 AM Bucknell SBDC \n",
|
|
"\n",
|
|
" Physical Address County NAICs \\\n",
|
|
"0 Philadelphia NaN \n",
|
|
"1 Delaware 531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\... \n",
|
|
"2 Lycoming 561720-JanitorialServices\\r\\r\\n\\r\\r\\n \n",
|
|
"3 Clarion 531120-LessorsofNonresidentialBuildings(except... \n",
|
|
"4 Union 312130-Wineries\\r\\r\\n\\r\\r\\n \n",
|
|
"\n",
|
|
" Primary NAICS NAICS_2 \\\n",
|
|
"0 NaN 0.0 \n",
|
|
"1 531390 - Other Activities Related to Real Esta... 53.0 \n",
|
|
"2 561720 - Janitorial Services \\r\\r\\n 56.0 \n",
|
|
"3 531120 - Lessors of Nonresidential Buildings (... 53.0 \n",
|
|
"4 312130 - Wineries \\r\\r\\n 31.0 \n",
|
|
"\n",
|
|
" PA NAICs Code Percentage PASBDC NAICs Code Percentage \n",
|
|
"0 0.000000 13.809955 \n",
|
|
"1 2.510127 2.723982 \n",
|
|
"2 3.605647 4.398190 \n",
|
|
"3 2.510127 2.723982 \n",
|
|
"4 2.876304 4.995475 "
|
|
]
|
|
},
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def tag_pasbdc_percentage(series):\n",
|
|
" if not pd.isna(series['NAICS_2']):\n",
|
|
" series[\"PASBDC NAICs Code Percentage\"] = (int(naics_value_counts[series['NAICS_2']]) / total_clients) * 100\n",
|
|
" return series\n",
|
|
"\n",
|
|
" return series\n",
|
|
"\n",
|
|
"client_naics_df = client_naics_df.apply(tag_pasbdc_percentage, axis=1)\n",
|
|
"client_naics_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"id": "1b445bdf-ef98-4026-95f7-139af5bcb052",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Save the data files\n",
|
|
"naics_df.to_csv(\"nasics_census.csv\", na_rep=\"\")\n",
|
|
"client_naics_df.to_csv(\"naics_client_list_tagged.csv\", na_rep=\"\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"id": "9437445a-9ed8-4e42-87ce-954f722aa5fe",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.plotly.v1+json": {
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"config": {
|
|
"plotlyServerURL": "https://plot.ly"
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},
|
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"data": [
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"hovertemplate": "NAICS_Label=%{x}<br>PASBDC NAICs Code Percentage=%{text}<extra></extra>",
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}
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},
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"name": "PASBDC Percentage",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
},
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|
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|
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|
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|
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|
"size": 12
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|
},
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|
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|
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|
},
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"showarrow": false,
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"text": "0.20%",
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|
"x": "22 - Utilities",
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|
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"y": 1.2131350574959792,
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|
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|
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|
},
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|
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|
"y": 7.206779107070672,
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"y": 3.8922670703282902,
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|
"x": "42 - Wholesale trade",
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|
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|
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|
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|
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|
},
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|
"x": "44 - Retail trade",
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|
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},
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|
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|
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"y": 9.652185879297146,
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"x": "48 - Transportation and warehousing",
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"y": 3.350635165005289,
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},
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"x": "49 - Transportation and warehousing",
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"y": 3.350635165005289,
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{
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},
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|
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|
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|
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|
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|
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|
},
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|
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|
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|
"y": 3.526090359425126,
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|
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|
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|
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|
"y": 14.07275370047046,
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|
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|
},
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|
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|
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|
"y": 1.5225468525574315,
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|
"size": 12
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|
},
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|
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|
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|
|
"y": 4.621610231446043,
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|
"size": 12
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|
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|
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|
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|
|
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|
|
"y": 2.245869492298033,
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|
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|
"size": 12
|
|
},
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|
"showarrow": false,
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|
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|
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|
|
"y": 17.948679355632198,
|
|
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|
},
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|
"size": 12
|
|
},
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|
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|
|
"text": "2.18%",
|
|
"x": "71 - Arts, entertainment, and recreation",
|
|
"xanchor": "center",
|
|
"y": 3.198832018852833,
|
|
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|
},
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|
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|
"size": 12
|
|
},
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|
"showarrow": false,
|
|
"text": "6.24%",
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|
"x": "72 - Accommodation and food services",
|
|
"xanchor": "center",
|
|
"y": 7.260533762223809,
|
|
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|
},
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|
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|
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|
"color": "#27323a",
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|
"size": 12
|
|
},
|
|
"showarrow": false,
|
|
"text": "11.52%",
|
|
"x": "81 - Other services (except public administration)",
|
|
"xanchor": "center",
|
|
"y": 12.532790339643412,
|
|
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|
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|
"size": 12
|
|
},
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|
"showarrow": false,
|
|
"text": "1.09%",
|
|
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|
|
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|
|
"y": 2.10116196062579,
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
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"title": {
|
|
"standoff": 15
|
|
},
|
|
"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
|
}
|
|
}
|
|
},
|
|
"width": 1500,
|
|
"xaxis": {
|
|
"anchor": "y",
|
|
"domain": [
|
|
0,
|
|
1
|
|
],
|
|
"title": {
|
|
"text": "Industry (NAICS Code)"
|
|
}
|
|
},
|
|
"yaxis": {
|
|
"anchor": "x",
|
|
"domain": [
|
|
0,
|
|
1
|
|
],
|
|
"range": [
|
|
0,
|
|
19.47262382922361
|
|
],
|
|
"tickformat": ".0f",
|
|
"title": {
|
|
"text": "Percentage of PASBDC Businesses"
|
|
}
|
|
}
|
|
}
|
|
},
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import plotly.express as px\n",
|
|
"import plotly.graph_objects as go\n",
|
|
"\n",
|
|
"# -------------------- Prepare NAICS mapping --------------------------------\n",
|
|
"naics_mapping = {}\n",
|
|
"for _, row in naics_df.iterrows():\n",
|
|
" split = str(row[\"NAICS_CODE\"]).split('-')\n",
|
|
" if len(split) == 2:\n",
|
|
" for code in range(int(split[0]), int(split[1]) + 1):\n",
|
|
" naics_mapping[code] = {\n",
|
|
" \"Industry\": row[\"NAICS2022_LABEL\"],\n",
|
|
" \"PA_Percentage\": float(row[\"Percentage\"])\n",
|
|
" }\n",
|
|
" else:\n",
|
|
" naics_mapping[int(row[\"NAICS_CODE\"])] = {\n",
|
|
" \"Industry\": row[\"NAICS2022_LABEL\"],\n",
|
|
" \"PA_Percentage\": float(row[\"Percentage\"])\n",
|
|
" }\n",
|
|
"\n",
|
|
"# -------------------- Clean and Aggregate Client Data -----------------------\n",
|
|
"client_naics_df[\"NAICS_2\"] = client_naics_df[\"NAICS_2\"].astype(float).astype(int)\n",
|
|
"\n",
|
|
"agg_df = (\n",
|
|
" client_naics_df\n",
|
|
" .groupby(\"NAICS_2\", as_index=False)\n",
|
|
" .agg({\n",
|
|
" \"PASBDC NAICs Code Percentage\": \"mean\",\n",
|
|
" \"PA NAICs Code Percentage\": \"mean\"\n",
|
|
" })\n",
|
|
" .sort_values(\"NAICS_2\")\n",
|
|
")\n",
|
|
"\n",
|
|
"# -------------------- Combine NAICS 31, 32, 33 ------------------------------\n",
|
|
"# Create a new column to group manufacturing codes\n",
|
|
"agg_df[\"NAICS_Combined\"] = agg_df[\"NAICS_2\"].apply(\n",
|
|
" lambda x: \"31-33\" if x in [31, 32, 33] else str(x)\n",
|
|
")\n",
|
|
"\n",
|
|
"# Aggregate the combined codes\n",
|
|
"combined_df = agg_df.groupby(\"NAICS_Combined\", as_index=False).agg({\n",
|
|
" \"PASBDC NAICs Code Percentage\": \"sum\", # Sum the percentages\n",
|
|
" \"PA NAICs Code Percentage\": \"sum\",\n",
|
|
" \"NAICS_2\": \"first\" # Keep first code for mapping\n",
|
|
"})\n",
|
|
"\n",
|
|
"# Update the NAICS_2 for the combined row to get proper mapping\n",
|
|
"combined_df.loc[combined_df[\"NAICS_Combined\"] == \"31-33\", \"NAICS_2\"] = 31\n",
|
|
"\n",
|
|
"# Add industry name from mapping\n",
|
|
"combined_df[\"Industry\"] = combined_df[\"NAICS_2\"].map(\n",
|
|
" lambda x: naics_mapping.get(x, {}).get(\"Industry\", \"Unknown\")\n",
|
|
")\n",
|
|
"\n",
|
|
"# For the combined manufacturing row, use a custom label\n",
|
|
"combined_df.loc[combined_df[\"NAICS_Combined\"] == \"31-33\", \"Industry\"] = \"Manufacturing\"\n",
|
|
"\n",
|
|
"# Get PA percentages - but don't sum for 31-33 since they're already the same value\n",
|
|
"combined_df[\"PA_Mapped_Percentage\"] = combined_df[\"NAICS_2\"].map(\n",
|
|
" lambda x: naics_mapping.get(x, {}).get(\"PA_Percentage\", None)\n",
|
|
")\n",
|
|
"\n",
|
|
"# Combine label for x-axis\n",
|
|
"combined_df[\"NAICS_Label\"] = combined_df[\"NAICS_Combined\"] + \" - \" + combined_df[\"Industry\"]\n",
|
|
"\n",
|
|
"# Sort to maintain logical order\n",
|
|
"combined_df = combined_df.sort_values(\"NAICS_Combined\")\n",
|
|
"\n",
|
|
"# -------------------- Plot PASBDC Bars --------------------------------------\n",
|
|
"fig = px.bar(\n",
|
|
" combined_df,\n",
|
|
" x=\"NAICS_Label\",\n",
|
|
" y=\"PASBDC NAICs Code Percentage\",\n",
|
|
" text=\"PASBDC NAICs Code Percentage\",\n",
|
|
" width=1500,\n",
|
|
" height=1000, \n",
|
|
")\n",
|
|
"fig.update_traces(marker_color=\"#6ebe4d\") \n",
|
|
"fig.update_traces(\n",
|
|
" name=\"PASBDC Percentage\",\n",
|
|
" texttemplate=\"%{text:.2f}%\",\n",
|
|
" textposition=\"outside\",\n",
|
|
" textfont_size=14\n",
|
|
")\n",
|
|
"\n",
|
|
"fig.add_trace(go.Scatter(\n",
|
|
" x=combined_df[\"NAICS_Label\"],\n",
|
|
" y=combined_df[\"PA_Mapped_Percentage\"],\n",
|
|
" name=\"PA Census Percentage\",\n",
|
|
" mode=\"lines+markers\",\n",
|
|
" line=dict(color=\"#27323a\"),\n",
|
|
" marker=dict(size=8)\n",
|
|
"))\n",
|
|
"\n",
|
|
"max_y = combined_df[[\"PASBDC NAICs Code Percentage\", \"PA_Mapped_Percentage\"]].max().max()\n",
|
|
"y_offset = max_y * 0.06 \n",
|
|
"\n",
|
|
"annotations = []\n",
|
|
"for x, y in zip(combined_df[\"NAICS_Label\"], combined_df[\"PA_Mapped_Percentage\"]):\n",
|
|
" annotations.append(dict(\n",
|
|
" x=x,\n",
|
|
" y=y + y_offset,\n",
|
|
" text=f\"{y:.2f}%\",\n",
|
|
" showarrow=False,\n",
|
|
" font=dict(size=12, color=\"#27323a\"),\n",
|
|
" xanchor=\"center\",\n",
|
|
" yanchor=\"bottom\"\n",
|
|
" ))\n",
|
|
"\n",
|
|
"fig.update_layout(\n",
|
|
" annotations=annotations,\n",
|
|
" bargap=0.1,\n",
|
|
" barmode=\"group\",\n",
|
|
" yaxis=dict(\n",
|
|
" range=[0, max_y * 1.15],\n",
|
|
" title=\"Percentage of PASBDC Businesses\",\n",
|
|
" tickformat=\".0f\"\n",
|
|
" ),\n",
|
|
" xaxis=dict(title=\"Industry (NAICS Code)\"),\n",
|
|
" legend=dict(x=0.02, y=0.98)\n",
|
|
")\n",
|
|
"\n",
|
|
"fig.show()\n",
|
|
"fig.write_image(\"comparison_graph.png\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b6368d88-2ea8-4a69-a1f1-cfe75789d597",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Tagging fips codes \n",
|
|
"---\n",
|
|
"It was not needed for this analysis, but in order to map counties to an actual map we need their fips identifiers. For this I used a library called addfips: https://pypi.org/project/addfips/"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"id": "16a45c4d-c034-4a92-ba35-bf1e14b51b34",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Physical Address County NAICS_2\n",
|
|
"Adams 45.0 13\n",
|
|
" 72.0 7\n",
|
|
" 81.0 6\n",
|
|
" 71.0 5\n",
|
|
" 11.0 4\n",
|
|
"Name: count, dtype: int64"
|
|
]
|
|
},
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"county_naics_df = pd.read_csv('naics_client_list_tagged.csv')\n",
|
|
"naics_value_counts = county_naics_df.groupby('Physical Address County')['NAICS_2'].value_counts()\n",
|
|
"\n",
|
|
"naics_value_counts.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "9985ca81-a4fa-4e80-8bd5-b02e705a9481",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import addfips\n",
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"client_naics_df = pd.read_csv('naics_tagged_client_list.csv')\n",
|
|
"county_naics_groups = client_naics_df.groupby('Physical Address County')\n",
|
|
"\n",
|
|
"# This gets the counts for non-null NAICS codes (including your 0s)\n",
|
|
"naics_value_counts = county_naics_groups['NAICS_2'].value_counts()\n",
|
|
"\n",
|
|
"# --- 💡 THIS IS THE FIX 💡 ---\n",
|
|
"# .size() gets the *total* number of clients in each county\n",
|
|
"# It includes rows where NAICS_2 is 0, valid, or NaN.\n",
|
|
"# This is your correct denominator.\n",
|
|
"total_clients_by_county = county_naics_groups.size()\n",
|
|
"# --- End Fix ---\n",
|
|
"\n",
|
|
"total_unique_counties = client_naics_df[client_naics_df['County Out of State'] == False]['Physical Address County'].unique()\n",
|
|
"\n",
|
|
"county_stats_list = []\n",
|
|
"af = addfips.AddFIPS()\n",
|
|
"\n",
|
|
"for county in total_unique_counties:\n",
|
|
" # Use .get() to avoid errors if a county has 100% NaN NAICS codes\n",
|
|
" # In that case, value_counts() would be empty for it\n",
|
|
" county_counts_series = naics_value_counts.get(county, pd.Series(dtype=float))\n",
|
|
" \n",
|
|
" missing_naics_count = 0\n",
|
|
" unique_valid_naics_count = len(county_counts_series) \n",
|
|
" \n",
|
|
" if 0 in county_counts_series.index:\n",
|
|
" # This line was always correct\n",
|
|
" missing_naics_count = county_counts_series.loc[0]\n",
|
|
" \n",
|
|
" # Decrement the *unique* count\n",
|
|
" unique_valid_naics_count -= 1\n",
|
|
"\n",
|
|
" # Now, just look up the true total for the county\n",
|
|
" total_records = total_clients_by_county.loc[county]\n",
|
|
"\n",
|
|
" fips = np.nan\n",
|
|
" if not pd.isna(county):\n",
|
|
" fips = af.get_county_fips(county, 'Pennsylvania')\n",
|
|
" \n",
|
|
" county_data = {\n",
|
|
" 'County': county,\n",
|
|
" 'fips': fips,\n",
|
|
" 'Unique Valid NAICS Codes': unique_valid_naics_count,\n",
|
|
" \"Missing NAICS\": missing_naics_count,\n",
|
|
" \"Total Clients\": total_records,\n",
|
|
" \"Percent NAICS Missing\": (missing_naics_count / total_records) * 100 if total_records > 0 else 0\n",
|
|
" }\n",
|
|
" \n",
|
|
" county_stats_list.append(county_data)\n",
|
|
"\n",
|
|
"county_stats_df = pd.DataFrame(county_stats_list)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "1ab22bb3-f6d9-4d74-8d09-f2a54620a36c",
|
|
"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>County</th>\n",
|
|
" <th>fips</th>\n",
|
|
" <th>Unique Valid NAICS Codes</th>\n",
|
|
" <th>Missing NAICS</th>\n",
|
|
" <th>Total Clients</th>\n",
|
|
" <th>Percent NAICS Missing</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>Clarion</td>\n",
|
|
" <td>42031</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>169</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>Elk</td>\n",
|
|
" <td>42047</td>\n",
|
|
" <td>18</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>112</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>Bradford</td>\n",
|
|
" <td>42015</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>56</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>58</th>\n",
|
|
" <td>Potter</td>\n",
|
|
" <td>42105</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>49</th>\n",
|
|
" <td>Bedford</td>\n",
|
|
" <td>42009</td>\n",
|
|
" <td>15</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" County fips Unique Valid NAICS Codes Missing NAICS Total Clients \\\n",
|
|
"3 Clarion 42031 21 0 169 \n",
|
|
"10 Elk 42047 18 0 112 \n",
|
|
"17 Bradford 42015 16 0 56 \n",
|
|
"58 Potter 42105 16 0 35 \n",
|
|
"49 Bedford 42009 15 0 37 \n",
|
|
"\n",
|
|
" Percent NAICS Missing \n",
|
|
"3 0.0 \n",
|
|
"10 0.0 \n",
|
|
"17 0.0 \n",
|
|
"58 0.0 \n",
|
|
"49 0.0 "
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"county_stats_df.sort_values(by='Percent NAICS Missing', ascending=True).head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a6f90567-0cd8-4865-a864-131ba06789bb",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Tagging fips codes\n",
|
|
"---\n",
|
|
"In order to generate a county heat map, we need to get a fips code for each county in the dataset. For this I used a library called addfips: https://pypi.org/project/addfips/"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"id": "fef2d7ff-0352-4f75-83f2-22c28be6b3fa",
|
|
"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>County</th>\n",
|
|
" <th>fips</th>\n",
|
|
" <th>Unique Valid NAICS Codes</th>\n",
|
|
" <th>Missing NAICS</th>\n",
|
|
" <th>Total Clients</th>\n",
|
|
" <th>Percent NAICS Missing</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Philadelphia</td>\n",
|
|
" <td>42101</td>\n",
|
|
" <td>23</td>\n",
|
|
" <td>280</td>\n",
|
|
" <td>1256</td>\n",
|
|
" <td>22.292994</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Delaware</td>\n",
|
|
" <td>42045</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>356</td>\n",
|
|
" <td>7.584270</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Lycoming</td>\n",
|
|
" <td>42081</td>\n",
|
|
" <td>17</td>\n",
|
|
" <td>36</td>\n",
|
|
" <td>144</td>\n",
|
|
" <td>25.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>Clarion</td>\n",
|
|
" <td>42031</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>169</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Union</td>\n",
|
|
" <td>42119</td>\n",
|
|
" <td>19</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>175</td>\n",
|
|
" <td>28.571429</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" County fips Unique Valid NAICS Codes Missing NAICS \\\n",
|
|
"0 Philadelphia 42101 23 280 \n",
|
|
"1 Delaware 42045 21 27 \n",
|
|
"2 Lycoming 42081 17 36 \n",
|
|
"3 Clarion 42031 21 0 \n",
|
|
"4 Union 42119 19 50 \n",
|
|
"\n",
|
|
" Total Clients Percent NAICS Missing \n",
|
|
"0 1256 22.292994 \n",
|
|
"1 356 7.584270 \n",
|
|
"2 144 25.000000 \n",
|
|
"3 169 0.000000 \n",
|
|
"4 175 28.571429 "
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def add_fips_apply(series):\n",
|
|
" af = addfips.AddFIPS()\n",
|
|
" return af.add_county_fips(series, county_field=\"County\", state='Pennsylvania')\n",
|
|
"\n",
|
|
"county_stats_df = county_stats_df.apply(add_fips_apply, axis=1)\n",
|
|
"county_stats_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "26109ebc-5700-4f0d-b6d3-262df944d2fd",
|
|
"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>County</th>\n",
|
|
" <th>fips</th>\n",
|
|
" <th>Unique Valid NAICS Codes</th>\n",
|
|
" <th>Missing NAICS</th>\n",
|
|
" <th>Total Clients</th>\n",
|
|
" <th>Percent NAICS Missing</th>\n",
|
|
" <th>Percent NAICS Coverage</th>\n",
|
|
" <th>NAICS to Census Gap</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Philadelphia</td>\n",
|
|
" <td>42101</td>\n",
|
|
" <td>23</td>\n",
|
|
" <td>280</td>\n",
|
|
" <td>1256</td>\n",
|
|
" <td>22.292994</td>\n",
|
|
" <td>92.0</td>\n",
|
|
" <td>8.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Delaware</td>\n",
|
|
" <td>42045</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>356</td>\n",
|
|
" <td>7.584270</td>\n",
|
|
" <td>84.0</td>\n",
|
|
" <td>16.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Lycoming</td>\n",
|
|
" <td>42081</td>\n",
|
|
" <td>17</td>\n",
|
|
" <td>36</td>\n",
|
|
" <td>144</td>\n",
|
|
" <td>25.000000</td>\n",
|
|
" <td>68.0</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>Clarion</td>\n",
|
|
" <td>42031</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>169</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>84.0</td>\n",
|
|
" <td>16.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Union</td>\n",
|
|
" <td>42119</td>\n",
|
|
" <td>19</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>175</td>\n",
|
|
" <td>28.571429</td>\n",
|
|
" <td>76.0</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" County fips Unique Valid NAICS Codes Missing NAICS \\\n",
|
|
"0 Philadelphia 42101 23 280 \n",
|
|
"1 Delaware 42045 21 27 \n",
|
|
"2 Lycoming 42081 17 36 \n",
|
|
"3 Clarion 42031 21 0 \n",
|
|
"4 Union 42119 19 50 \n",
|
|
"\n",
|
|
" Total Clients Percent NAICS Missing Percent NAICS Coverage \\\n",
|
|
"0 1256 22.292994 92.0 \n",
|
|
"1 356 7.584270 84.0 \n",
|
|
"2 144 25.000000 68.0 \n",
|
|
"3 169 0.000000 84.0 \n",
|
|
"4 175 28.571429 76.0 \n",
|
|
"\n",
|
|
" NAICS to Census Gap \n",
|
|
"0 8.0 \n",
|
|
"1 16.0 \n",
|
|
"2 32.0 \n",
|
|
"3 16.0 \n",
|
|
"4 24.0 "
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"total_naics_count = 0 \n",
|
|
"\n",
|
|
"for index, row in naics_df.iterrows():\n",
|
|
" split = str(row[\"NAICS_CODE\"]).split('-')\n",
|
|
" if len(split) == 2:\n",
|
|
" total_naics_count += abs(int(split[0]) - int(split[1])) + 1\n",
|
|
" else:\n",
|
|
" total_naics_count += 1\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"county_stats_df['Percent NAICS Coverage'] = (county_stats_df['Unique Valid NAICS Codes'] / total_naics_count) * 100\n",
|
|
"county_stats_df['NAICS to Census Gap'] = 100 - county_stats_df['Percent NAICS Coverage']\n",
|
|
"\n",
|
|
"county_stats_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"id": "9d77a878-0fe8-45c3-9468-295e64b18581",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"14.87% of NAICS codes are missing network wide\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"total_clients = county_stats_df['Total Clients'].sum()\n",
|
|
"missing_naics = county_stats_df['Missing NAICS'].sum()\n",
|
|
"percent_missing_naics = missing_naics / total_clients\n",
|
|
"print(f\"{percent_missing_naics * 100:.2f}% of NAICS codes are missing network wide\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9950718e-1495-4d89-b5a2-dc34f1bcd6d4",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Generating the Choropleth map\n",
|
|
"---\n",
|
|
"Followed directly from documentation here: https://plotly.com/python/choropleth-maps/"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"id": "82a73940-b35a-4fd1-a75a-36f78ead7895",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_19455/3089456622.py:19: UserWarning:\n",
|
|
"\n",
|
|
"Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
|
|
"\n",
|
|
"\n",
|
|
"/tmp/ipykernel_19455/3089456622.py:22: UserWarning:\n",
|
|
"\n",
|
|
"Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
|
|
"\n",
|
|
"\n",
|
|
"/tmp/ipykernel_19455/3089456622.py:23: UserWarning:\n",
|
|
"\n",
|
|
"Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.plotly.v1+json": {
|
|
"config": {
|
|
"plotlyServerURL": "https://plot.ly"
|
|
},
|
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"data": [
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{
|
|
"coloraxis": "coloraxis",
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|
"customdata": [
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0,
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1.1363636363636365,
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0,
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0,
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22.29299363057325,
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28.57142857142857,
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0,
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[
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],
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"featureidkey": "properties.fips",
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"geo": "geo",
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"geojson": {
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"bbox": [
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"features": [
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"coordinates": [
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"type": "Polygon"
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},
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"id": "0",
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"properties": {
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|
"CENSUSAREA": 525.8,
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|
"COUNTY": "013",
|
|
"County": "Blair",
|
|
"GEO_ID": "0500000US42013",
|
|
"LSAD": "County",
|
|
"Missing NAICS": 8,
|
|
"NAICS to Census Gap": 28,
|
|
"NAME": "Blair",
|
|
"Percent NAICS Coverage": 72,
|
|
"Percent NAICS Missing": 7.766990291262135,
|
|
"STATE": "42",
|
|
"Total Clients": 103,
|
|
"Unique Valid NAICS Codes": 18,
|
|
"fips": "42013",
|
|
"id": "42013",
|
|
"label_text": "<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Blair<br>7.77%</span>",
|
|
"lat": 40.477236847861334,
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"lon": -78.35055711877322,
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"percent_str": "7.77%"
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},
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"type": "Feature"
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},
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{
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"bbox": [
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Blair<br>7.77%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Bradford<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Cambria<br>1.14%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Clarion<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Elk<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Indiana<br>25.93%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Lackawanna<br>9.41%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Mifflin<br>23.53%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Montour<br>27.91%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Potter<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Sullivan<br>10.53%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Warren<br>7.69%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Armstrong<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Allegheny<br>17.66%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Beaver<br>23.29%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Bucks<br>28.35%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Cameron<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Centre<br>19.41%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Chester<br>35.63%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Clearfield<br>0.88%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Crawford<br>5.41%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Delaware<br>7.58%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Erie<br>9.54%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Fayette<br>3.03%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Franklin<br>1.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Huntingdon<br>11.76%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Juniata<br>38.46%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Lawrence<br>19.3%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Luzerne<br>8.36%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Mercer<br>9.52%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Monroe<br>10.53%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Northampton<br>25.45%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Philadelphia<br>22.29%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Snyder<br>22.95%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Somerset<br>11.11%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Susquehanna<br>4.55%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Union<br>28.57%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Venango<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Washington<br>17.7%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">York<br>7.2%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Berks<br>7.82%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Butler<br>17.09%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Carbon<br>9.21%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Columbia<br>12.96%</span>",
|
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Forest<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Fulton<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Lebanon<br>12.28%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Lycoming<br>25.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Perry<br>16.67%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Tioga<br>2.44%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Wyoming<br>9.76%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Bedford<br>0.0%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Clinton<br>14.55%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Cumberland<br>23.87%</span>",
|
|
"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Dauphin<br>11.01%</span>",
|
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Greene<br>12.5%</span>",
|
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Jefferson<br>0.79%</span>",
|
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Lancaster<br>8.19%</span>",
|
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Lehigh<br>30.28%</span>",
|
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">McKean<br>0.0%</span>",
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Schuylkill<br>10.77%</span>",
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Wayne<br>6.25%</span>",
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Westmoreland<br>7.11%</span>",
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Montgomery<br>18.13%</span>",
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Northumberland<br>26.03%</span>",
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Pike<br>13.64%</span>",
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"<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">Adams<br>3.08%</span>"
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|
|
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|
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|
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|
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|
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|
"color": "black",
|
|
"size": 22
|
|
},
|
|
"text": "NAICS Coverage Per County (FY 2025)",
|
|
"x": 0.5,
|
|
"xanchor": "center",
|
|
"yanchor": "top"
|
|
},
|
|
"width": 1300
|
|
}
|
|
}
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import geopandas as gpd\n",
|
|
"import pandas as pd\n",
|
|
"import plotly.express as px\n",
|
|
"import plotly.graph_objects as go\n",
|
|
"\n",
|
|
"def make_county_penetration_map(county_stats_df, value_column, title, out_image_path=\"county_heat_map.png\", tick_suffix=\"%\", color_continuous_scale=['#101c25', '#bad4d6','#79be54']):\n",
|
|
" # This file has the FIPS codes and the county shape (geometry)\n",
|
|
" geojson_url = 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'\n",
|
|
" gdf = gpd.read_file(geojson_url)\n",
|
|
" \n",
|
|
" # We merge your data *onto* the geospatial data\n",
|
|
" merged_gdf = gdf.merge(county_stats_df, left_on='id', right_on='fips')\n",
|
|
" \n",
|
|
" # Filter for Pennsylvania (FIPS prefix '42') \n",
|
|
" pa_gdf = merged_gdf[merged_gdf['fips'].str.startswith('42')].copy()\n",
|
|
" \n",
|
|
" #Calculate the centroid for each county's geometry\n",
|
|
" #Note: This might warn about CRS, but for plotting it's usually fine.\n",
|
|
" pa_gdf['centroid'] = pa_gdf.geometry.centroid\n",
|
|
" \n",
|
|
" # Extract needed positional data\n",
|
|
" pa_gdf['lon'] = pa_gdf.centroid.x\n",
|
|
" pa_gdf['lat'] = pa_gdf.centroid.y\n",
|
|
" \n",
|
|
" pa_gdf = pa_gdf.drop(columns=['centroid'])\n",
|
|
" \n",
|
|
" # Create the text label\n",
|
|
" pa_gdf['percent_str'] = (pa_gdf[value_column].round(2).astype(str)) + tick_suffix \n",
|
|
" # Plotly accepts html styling in text labels\n",
|
|
" pa_gdf['label_text'] = '<span style=\"font-weight:bold; color:white; text-shadow: 1px 1px 2px black;\">' + pa_gdf['County'] + '<br>' + pa_gdf['percent_str'] + \"</span>\"\n",
|
|
" \n",
|
|
" \n",
|
|
" # Create the Base Choropleth Map \n",
|
|
" fig = px.choropleth(pa_gdf,\n",
|
|
" geojson=pa_gdf,\n",
|
|
" locations='fips',\n",
|
|
" featureidkey='properties.fips',\n",
|
|
" color=value_column, # Column for color\n",
|
|
" hover_name='County',\n",
|
|
" hover_data={value_column: ':.2f', 'fips': False},\n",
|
|
" color_continuous_scale=color_continuous_scale,\n",
|
|
" range_color=[0, 100]\n",
|
|
" )\n",
|
|
" \n",
|
|
" \n",
|
|
" # Add a scatter plot of the state labels \n",
|
|
" fig.add_trace(\n",
|
|
" go.Scattergeo(\n",
|
|
" lat=pa_gdf['lat'],\n",
|
|
" lon=pa_gdf['lon'],\n",
|
|
" text=pa_gdf['label_text'],\n",
|
|
" mode='text',\n",
|
|
" hoverinfo='none'\n",
|
|
" )\n",
|
|
" )\n",
|
|
" \n",
|
|
" \n",
|
|
" \n",
|
|
" fig.update_geos(\n",
|
|
" fitbounds=\"locations\", \n",
|
|
" visible=False, \n",
|
|
" projection_scale=60,\n",
|
|
" center={\"lat\": pa_gdf['lat'].mean(), \"lon\": pa_gdf['lon'].mean()}\n",
|
|
" )\n",
|
|
" \n",
|
|
" fig.update_coloraxes(\n",
|
|
" colorbar=dict(\n",
|
|
" title=value_column,\n",
|
|
" ticksuffix=\"%\", \n",
|
|
" )\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Set the layout for saving an image\n",
|
|
" fig.update_layout(\n",
|
|
" title=dict(\n",
|
|
" text=title,\n",
|
|
" font=dict(size=22, color='black'),\n",
|
|
" x=0.5, # center the title horizontally (0=left, 1=right)\n",
|
|
" xanchor='center',\n",
|
|
" yanchor='top'\n",
|
|
" ),\n",
|
|
" margin={\"r\":0,\"t\":80,\"l\":0,\"b\":0},\n",
|
|
" width=2000,\n",
|
|
" height=1200,\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Write the big image then size down for display here\n",
|
|
" fig.write_image(out_image_path)\n",
|
|
" fig.update_layout(\n",
|
|
" title=dict(\n",
|
|
" text=title,\n",
|
|
" font=dict(size=22, color='black'),\n",
|
|
" x=0.5, # center the title horizontally (0=left, 1=right)\n",
|
|
" xanchor='center',\n",
|
|
" yanchor='top'\n",
|
|
" ),\n",
|
|
" margin={\"r\":0,\"t\":80,\"l\":0,\"b\":0},\n",
|
|
" width=1300,\n",
|
|
" height=800,\n",
|
|
" )\n",
|
|
" fig.show()\n",
|
|
" \n",
|
|
" \n",
|
|
"make_county_penetration_map(county_stats_df, \"Percent NAICS Missing\", \n",
|
|
" title=\"Missing Client NAICS Codes Per County (FY 2025)\", \n",
|
|
" out_image_path=\"county_missing_heat_map.png\", \n",
|
|
" color_continuous_scale=[\n",
|
|
" '#cde0c3',\n",
|
|
" '#b4e09a',\n",
|
|
" #'#9ed67d',\n",
|
|
" \n",
|
|
" '#6dafb2',\n",
|
|
" '#499699',\n",
|
|
" '#2d797c',\n",
|
|
" '#256e70',\n",
|
|
" \n",
|
|
" '#156264',\n",
|
|
" '#094f51',\n",
|
|
" '#004649',\n",
|
|
"\n",
|
|
" '#003234',\n",
|
|
" '#002f30',\n",
|
|
" '#002223',\n",
|
|
" '#001111'\n",
|
|
" ]\n",
|
|
" );\n",
|
|
"\n",
|
|
"make_county_penetration_map(county_stats_df, \"Percent NAICS Coverage\", title=\"NAICS Coverage Per County (FY 2025)\", out_image_path=\"county_coverage_heat_map.png\");\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "4d1b253a-2cbd-474c-bb7c-ed7e1b9a98ae",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f400daa5-caae-45dc-81b1-5092090d7db3",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Creating the word document\n",
|
|
"---\n",
|
|
"Now we will generate a word document with these graphs on it"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"id": "8af495fa-aa0b-41e4-9a7f-d411e5d4b5eb",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from pathlib import Path\n",
|
|
"import sys\n",
|
|
"from docx import Document\n",
|
|
"from docx.shared import Inches, Pt, RGBColor\n",
|
|
"from docx.enum.text import WD_ALIGN_PARAGRAPH\n",
|
|
"\n",
|
|
"notebook_dir = Path().resolve() # Current working directory\n",
|
|
"project_root = notebook_dir.parent # Goes up to root/\n",
|
|
"sys.path.insert(0, str(project_root / \"libs\"))\n",
|
|
"\n",
|
|
"from word_library import WordDocumentBuilder, PageConfig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"id": "ba7cb145-7866-4c5f-80cb-6c611242199e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def naics_penetration_page_one(builder: WordDocumentBuilder, sector_table:str=\"sector_table.jpg\", penetration_graph:str=\"comparison_graph.png\"):\n",
|
|
" \n",
|
|
" for section in builder.doc.sections:\n",
|
|
" section.top_margin = Inches(0.5)\n",
|
|
" section.bottom_margin = Inches(0.5)\n",
|
|
" section.left_margin = Inches(0.5)\n",
|
|
" section.right_margin = Inches(0.5)\n",
|
|
" heading_paragraph = builder.doc.add_paragraph()\n",
|
|
"\n",
|
|
" # TODO: replace this part with using the section numbners of the document builder\n",
|
|
" heading_run = heading_paragraph.add_run(f\"1.1 NAICS Penetration Study\") \n",
|
|
" heading_run.font.name = 'Futera'\n",
|
|
" heading_run.font.size = Pt(12)\n",
|
|
" heading_run.font.color.rgb = RGBColor(113, 191, 68)\n",
|
|
" heading_run.bold = True\n",
|
|
"\n",
|
|
" penetration_picture = builder.doc.add_picture(penetration_graph, width=Inches(7))\n",
|
|
" last_pg = builder.doc.paragraphs[-1]\n",
|
|
" last_pg.alignment = WD_ALIGN_PARAGRAPH.CENTER\n",
|
|
"\n",
|
|
" # TODO: Change this to use section number and prefix in script\n",
|
|
" pen_note_run = builder.doc.paragraphs[-1].add_run(f\"\\nFigure {\"1.1\"}.{builder.figure_number + 1} shows the percentage of PASBDC clients per NAICS code compared to census percentages.\")\n",
|
|
" pen_note_run.font.name = 'Futera'\n",
|
|
" pen_note_run.font.size = Pt(8)\n",
|
|
" pen_note_run.font.color.rgb = RGBColor(15, 27, 38)\n",
|
|
" pen_note_run.bold = True\n",
|
|
"\n",
|
|
" builder.figure_number += 1\n",
|
|
" # Create a table to hold the sector table and an explaination\n",
|
|
" \n",
|
|
" picture_table = builder.doc.add_table(rows=2, cols=2)\n",
|
|
"\n",
|
|
" row1_cells = picture_table.rows[0].cells\n",
|
|
" row2_cells = picture_table.rows[1].cells\n",
|
|
"\n",
|
|
" sector_chart_paragrah = row1_cells[0].paragraphs[0]\n",
|
|
" sector_chart_run = sector_chart_paragrah.add_run()\n",
|
|
" sector_chart_run.add_picture(sector_table, width=Inches(2.5), height=Inches(3.5))\n",
|
|
"\n",
|
|
" sector_chart_note_paragraph = row2_cells[0].paragraphs[0]\n",
|
|
" # TODO: Change this to use section number and prefix in script\n",
|
|
" sector_note_run = sector_chart_note_paragraph.add_run(f\"Table {\"1.1\"}.{builder.table_number + 1} Shows the NAICS codes for each sector along with the percentage that sector makes up of the PA census data.\")\n",
|
|
" sector_note_run.font.name = 'Futera'\n",
|
|
" sector_note_run.font.size = Pt(7)\n",
|
|
" sector_note_run.font.color.rgb = RGBColor(15, 27, 38)\n",
|
|
" sector_note_run.bold = True\n",
|
|
"\n",
|
|
" builder.table_number += 1\n",
|
|
"\n",
|
|
" county_chart_note_paragraph = row2_cells[1].paragraphs[0]\n",
|
|
" \n",
|
|
" census_note_run = county_chart_note_paragraph.add_run(f\"NOTE: The US census does not cover agriculture (as that is the responsiblity of the USDA) nor government institutions (as the census measures the private sector). Agriculture data was soruced from the USDA where number of farm operations was used as the statistical stand in. As for government institutions, that data was sourced from the BLS by taking the number of NAICS code 92 organizations.\")\n",
|
|
" census_note_run.font.name = 'Futera'\n",
|
|
" census_note_run.font.size = Pt(6)\n",
|
|
" census_note_run.font.color.rgb = RGBColor(15, 27, 38)\n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"id": "4d2804fb-69de-4116-83b8-fd99b2eea8ee",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# TODO: Change the percent missing naics parameter to default to something super stupid like a negative number so its obvious when making the script\n",
|
|
"\n",
|
|
"def naics_penetration_page_two(builder: WordDocumentBuilder, county_map:str=\"county_missing_heat_map.png\", percent_missing_naics:float = percent_missing_naics):\n",
|
|
" # Add the county heat map\n",
|
|
" sector_chart_paragrah = builder.doc.add_paragraph()\n",
|
|
" sector_chart_run = sector_chart_paragrah.add_run()\n",
|
|
" sector_chart_run.add_picture(county_map, width=Inches(7))\n",
|
|
"\n",
|
|
" last_pg = builder.doc.paragraphs[-1]\n",
|
|
" last_pg.alignment = WD_ALIGN_PARAGRAPH.CENTER\n",
|
|
"\n",
|
|
" county_chart_note_paragraph = builder.doc.add_paragraph()\n",
|
|
" # TODO: Change this to use section number and prefix in script\n",
|
|
" county_note_run = county_chart_note_paragraph.add_run(f\"Figure {\"1.1\"}.{builder.figure_number + 1} shows the percentage of clients whose NAICS codes were missing in Neoserra by county. Network wide, {percent_missing_naics * 100:.2f}% of client profiles are missing NAICS codes.\")\n",
|
|
" county_note_run.font.name = 'Futera'\n",
|
|
" county_note_run.font.size = Pt(7)\n",
|
|
" county_note_run.font.color.rgb = RGBColor(15, 27, 38)\n",
|
|
" county_note_run.bold = True\n",
|
|
"\n",
|
|
" builder.figure_number += 1\n",
|
|
"\n",
|
|
" pass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"id": "1bf0a9cb-2b75-46f7-bccb-488eeb90c1c7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pages = [\n",
|
|
" PageConfig(naics_penetration_page_one, add_page_break=False),\n",
|
|
" PageConfig(naics_penetration_page_two, add_page_break=False)\n",
|
|
"]\n",
|
|
"\n",
|
|
"builder = WordDocumentBuilder()\n",
|
|
"\n",
|
|
"doc = builder.create_document(\n",
|
|
" pages,\n",
|
|
" \"section1_1.docx\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "1dd7fb41-4eb9-45da-b73a-c1b2814e932a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7c1402bb-1f87-40b6-b210-bb164f7bf6c8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "fa4c1fc7-d6b6-40f5-8652-d24b2e83a3fb",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|