Sector Centrality 2021 U.S I/O tables

Finally got around to passing the most recent data into the page rank algo. I didn't weight it this time because I didn't have the industry by industry labor content and I don't know what to make of the sector-sector prices (I'm using this https://apps.bea.gov/iTable/?reqid=150&step=2&isuri=1&categories=Io#eyJhcHBpZCI6MTUwLCJzdGVwcyI6WzEsMiwzXSwiZGF0YSI6W1siY2F0ZWdvcmllcyIsIklPIl0sWyJUYWJsZV9MaXN0IiwiNjAwMCJdXX0= ) so this is just plain page-rank. 

Notable that Machinery, Computers & Electronics, Motor Vehicles (and associated products) score the highest and that wood products, minerals, and metals score the lowest.

Where'd I find the labor content for these same sectors?

Anyway, here's the PageRank Centrality for U.S I/O 2021  

['Farms6.9843573300080015' 'Forestry, fishing, and related activities3.0189546941902115' 'Oil and gas extraction2.4362916920056077' 'Mining, except oil and gas3.2183699815244164' 'Support activities for mining2.556057468796985' 'Utilities2.443260441544314e-05' 'Construction2.443260441544314e-05' 'Wood products1.6151163990775356e-15' 'Nonmetallic mineral products7.019023566910198e-16' 'Primary metals2.5641896891960762e-17' 'Fabricated metal products0.2619941111955933' 'Machinery11.309162297595659' 'Computer and electronic products11.309162297595659' 'Electrical equipment, appliances, and components2.638519093077346' 'Motor vehicles, bodies and trailers, and parts11.13908067852122' 'Other transportation equipment1.7429297553268426' 'Furniture and related products1.7429297553268426' 'Miscellaneous manufacturing1.7897991053934397' 'Food and beverage and tobacco products1.7505760226160056' 'Textile mills and textile product mills1.1346367883413317' 'Apparel and leather and allied products1.1346367883413317' 'Paper products1.7073428749082717' 'Printing and related support activities1.7073428749082717' 'Petroleum and coal products0.0019571791339500415' 'Chemical products1.4164465838584883' 'Plastics and rubber products1.2612479978414193' 'Wholesale trade0.8553208927587204' 'Motor vehicle and parts dealers2.5990169407552055' 'Food and beverage stores2.211494284941866' 'General merchandise stores2.2032977007611674' 'Other retail2.276196282876631' 'Air transportation2.276196282876631' 'Rail transportation1.8815700378724656' 'Water transportation1.8815700378724656' 'Truck transportation2.079903410584055' 'Transit and ground passenger transportation0.30158717938543234' 'Pipeline transportation0.30158717938543234' 'Other transportation and support activities1.8243278581437476' 'Warehousing and storage0.4072398609007258' 'Publishing industries, except internet (includes software)0.47412875287058237' 'Motion picture and sound recording industries0.47412875287058237' 'Broadcasting and telecommunications0.580807599321912' 'Data processing, internet publishing, and other information services0.9572158098729701' 'Federal Reserve banks, credit intermediation, and related activities0.7014336375138344' 'Securities, commodity contracts, and investments0.5066563382522222' 'Insurance carriers and related activities2.4737577125019174' 'Funds, trusts, and other financial vehicles0.06367229495628614' 'Housing2.009833037000016' 'Other real estate0.130772851106417' 'Rental and leasing services and lessors of intangible assets0.006125544062927019' 'Legal services0.0026644909241400534' 'Computer systems design and related services0.012889769875216776' 'Miscellaneous professional, scientific, and technical services0.016599046902375205' 'Management of companies and enterprises0.007418421129861582' 'Administrative and support services0.01124114365560956' 'Waste management and remediation services0.01124114365560956' 'Educational services0.02583831709865851' 'Ambulatory health care services0.039526314303911785' 'Hospitals0.04053884521311496' 'Nursing and residential care facilities0.04053884521311496' 'Social assistance0.023248469357163938' 'Performing arts, spectator sports, museums, and related activities0.023248469357163938' 'Amusements, gambling, and recreation industries0.0010554100686806852' 'Accommodation9.732384672752608e-05' 'Food services and drinking places0.0018095081607738858' 'Other services, except government0.0005740234597283036' 'Federal general government (defense)0.0005183043839862668' 'Federal general government (nondefense)0.0005161061939674822' 'Federal government enterprises0.0001675568673399331' 'State and local general government0.0001675568673399331' 'State and local government enterprises0.00026952858784066807' 'Scrap, used and secondhand goods0.00013599140873930937' 'Noncomparable imports and rest-of-the-world adjustment1.0898308995052032e-05']

and the google Colab code:

from google.colab import files
import pandas as pd

# Upload the file
uploaded = files.upload()

import numpy as np
import matplotlib.pyplot as plt 
from numpy import linalg as LA

# Assuming you've uploaded the 'table.csv' file and loaded it into the 'data' DataFrame
data = pd.read_csv('TableCCI-O.csv', skiprows=4, skipfooter=4, engine='python')

IO = data.iloc[:, 2:].values

# Now 'data' contains the DataFrame with the correct delimiters
print(IO)
print(LA.inv(IO))

theta = 0.0
gamma = 0.85 
N = 73

def Adj(M): return 1.0*(M>0) #connectivity

def C(M): return 1.0*(M==0.0) #correction

#def WaP(M): return ((1/(M+C(M)))-C(M))*Adj(M) #weight against price

def WaP(M): return M*Adj(M) #weight at price

def S(M): return np.diag(1/(WaP(M)@(np.ones(N).T))) #out strength

def D(M): return np.diag(1/(Adj(M)@(np.ones(N).T))) #out degree

def WPR1(M): return ((theta*S(M)@WaP(M)) + ((1-theta)*D(M)@Adj(M))).T 

def WPR2(M): return (gamma*WPR1(M)) + ((1-gamma)*np.ones((N,N))/N) #weighted page rank matrix 

print(Adj(IO))

print(WaP(IO))

print(WPR2(IO))

eigval, eigvec = LA.eig(WPR2(IO))
names = data.iloc[:, 1:2].values.flatten()
rank = 100*abs(eigvec[0])/np.sum(abs(eigvec[0]))

srank = rank.astype('str')

results = names + srank  #bug
print(results) #weighted page rank