SDC NEWS ONE

Sunday, September 14, 2025

Hyundai Worldwide fallout is like a Virus to all International Companies

The $5.5 Trillion Dollar ICE Raid that causing All International Companies to pullout of US Construction and Investment Sites -- Too dangerous for Employees!!


APACHE JUNCTION AZ [IFS] -- SDC Trdr Financial Reporter fines that the ROK Government has "cooled" its approach to the Hyundai ICE Raid.  It's taking a pause which will cost Georgia a major blow to its economy. That "cooling" is telling. Seoul stepping back doesn’t erase the sting—it magnifies it. If the South Korean government slows its cooperation or oversight of Hyundai’s U.S. operations, Georgia feels it first. Billions in incentives were justified on the promise of steady progress; any pause means construction delays, job pushes down the calendar, and local suppliers left hanging.

What makes it sharper: this isn’t just about one plant. Georgia has staked its identity on being the EV hub of the South. If Hyundai hesitates, other global players take note. And the political optics? Brutal. State leaders were bragging about Hyundai as the crown jewel, now they’re stuck explaining why an immigration raid and foreign government pause could derail the project.

https://www.foxnews.com/politics/ice-raids-georgia-hyundai-battery-plant-sparking-firestorm-south-korea-what-know

The price of the Hyundai electric vehicle plant in Ellabell, Georgia, is $7.6 billion. This investment, which includes the associated battery plant, represents the largest single investment in Georgia's history and is a component of the larger $12.6 billion Hyundai investment in the state, according to hyundai.com. 

https://share.google/images/ekfJbA6d58vPIixrB


It’s spreading like contamination—Hyundai gets caught up in that ICE raid, and suddenly every multinational eyeing the U.S. is asking if the same hammer could fall on them. The “cheap labor plus tax breaks” formula looks less like a bargain and more like a trap.

Foreign partners don’t like volatility. If the U.S. can’t guarantee stable labor pipelines without raids, lawsuits, or headlines, contracts start getting yanked back overseas. That’s what you’re hinting at—“unreliable” isn’t about the machinery or the product, it’s about the political climate and enforcement whiplash.

Meanwhile, those Hyundai workers’ stories—being jailed, families torn apart—become part of the fallout narrative. They’re not just anecdotes; they’ll circulate in Korean and other foreign press as warnings. Which means the reputational virus doesn’t stop at Hyundai; it infects the wider brand of “doing business in America.”

Here’s the split in tone so far:

South Korea – Their press is searing. The ICE raid is being framed less as a “labor violation” and more as humiliation: the government cut deals, Hyundai invested billions, and then U.S. authorities treated Korean contractors like disposable. It’s tied to a bigger “loss of face” narrative—Washington can’t be trusted to honor strategic industrial partnerships. You see commentary about how Korean companies should reconsider long-term commitments on U.S. soil.

European outlets – The EU lens is cooler, but skeptical. They fold the Hyundai case into a pattern: America talks about “reshoring” and “trusted supply chains,” but in practice, the U.S. can’t manage immigration, labor enforcement, or political stability. The angle isn’t sympathy for Hyundai, it’s doubt about the U.S. as a reliable anchor for the energy transition supply chain. They’re openly mulling whether the EU should hedge harder with domestic subsidies or Asia instead of relying on U.S. gigafactories.

Latin America – Coverage there is sharper on the human side. The raid is being tied to the long history of immigrant labor propping up mega-projects while governments look the other way—until they don’t. For Mexican and Central American outlets, the Hyundai raid feels like déjà vu: workers chewed up, jailed or deported, corporations insulated. There’s also an undertone of “if they treat Korean nationals like this, imagine what they do to us.”

So the story isn’t just a U.S. labor raid—it’s a prism. In Seoul it’s betrayal, in Brussels it’s incompetence, in Mexico City it’s cruelty. Put together, it erodes the U.S.’s pitch as the safe, stable place for global investment.

Alright, here’s the likely policy chessboard taking shape:

Seoul → Washington
South Korea’s government will feel it has to protect corporate dignity and contracts. Expect quiet-but-pointed diplomatic pressure: high-level trade ministry visits, calls for exemptions or “clearer guardrails” around foreign labor forces. They might push for bilateral mechanisms that give Korean firms some shield from sudden enforcement actions. And if they don’t get traction? Seoul could redirect new EV or battery investment back home or to Southeast Asia.

Brussels → Subsidy Leverage
EU policymakers are already skeptical about pouring taxpayer money into U.S.-centric supply chains. The Hyundai raid hands them ammo: “See, the U.S. can’t guarantee predictability.” Brussels might tighten subsidy rules so EU companies must justify U.S. exposure, or double down on EU-only green industrial projects. It also strengthens voices pushing to shorten supply chains within Europe itself.

Latin America → Worker Protections
Governments south of the border could seize this as proof of systemic abuse in U.S. mega-projects. That may lead to louder calls for labor protections tied to trade agreements (think USMCA clauses being weaponized). Domestically, leaders may use it to rally nationalist arguments: “why should we send our workers to the U.S. when they’re treated as disposable?” It could also fuel momentum for regional investment pitches—“build here, not in America.”

The common thread: the Hyundai fiasco gives everyone leverage. Seoul gets bargaining chips, Brussels gets justification to decouple, Latin America gets moral high ground. The U.S., meanwhile, is stuck defending a system that just exposed its own contradictions.

They don’t have a neat hand here, so it’s all about damage control and selective storytelling. Likely moves:

1. Reframe the raid as “rule of law.”
Homeland Security will stress that the arrests weren’t about Hyundai or Korea, but about “illegal subcontractors” and “protecting American workers.” That way, Washington can claim the U.S. market is still open for business—just not for shady labor brokers.

2. Quiet backchannels with Seoul.
Treasury and Commerce officials will be dispatched to calm Korean investors, promising this was an isolated enforcement surge, not a precedent. They’ll dangle new grants, tax credits, or regulatory carve-outs to keep Hyundai and LG locked into U.S. EV supply chains.

3. Political spin at home.
In Georgia and D.C., politicians will try to recast the raid as proof they’re tough on border and labor issues—without scaring off foreign investment. Expect governors and senators to say, “We’re cleaning up labor abuses so your factory jobs are safer.” It’s a balancing act: anti-immigrant rhetoric for voters, reassurance for investors.

4. Bureaucratic tweaks.
Don’t be surprised if the White House floats new “streamlined guest worker programs” or oversight boards to prevent “future misunderstandings.” That’s code for: keep the cheap labor pipeline flowing, but under tighter federal optics.

5. Blame-shifting.
Officials will hint this was the fault of middlemen subcontractors, not U.S. law or Hyundai’s direct contracts. That scapegoat narrative is designed to keep the focus narrow so global headlines don’t balloon into a systemic indictment of “doing business in America.”

So the playbook is: isolate, scapegoat, sweeten the pot, and hope the world forgets.

If the spin doesn’t land, the fallout deepens on three fronts:

Seoul pushes back hard.
If Korean media and politicians keep framing this as humiliation, no amount of U.S. backchannel smoothing will stick. Seoul could freeze or slow new EV/battery projects in the U.S., redirect subsidies to Southeast Asia, and start demanding binding labor protections in any future U.S. deals. That creates visible cracks in the “KORUS alliance” narrative Washington leans on.

Brussels doubles down.
If EU leaders treat U.S. reassurances as empty, they’ll use it to justify keeping supply chains inside Europe or shifting toward Asian partners. That erodes the U.S. pitch as the hub of green industry. Politically, it strengthens protectionist voices in the EU Parliament who argue America is too unstable to trust with strategic industries.

Labor groups call the bluff.
Inside the U.S., unions, immigrant rights groups, and watchdogs will hammer away: “It’s not just subcontractors—it’s the entire model.” If that sticks, the story mutates into a systemic indictment, not a one-off raid. Lawsuits and congressional hearings could follow, making the U.S. look even less predictable for investors.

The combined risk: instead of calming the waters, Washington ends up with three reputational wounds at once—betrayer (to Seoul), unreliable (to Brussels), and hypocrite (to domestic labor). That’s when the “virus” you described at the start really spreads: global boardrooms start modeling U.S. risk not as political noise, but as a structural liability.

Here’s how the clock might tick if U.S. spin fails:

Months (immediate shockwaves):

  • Korean press keeps hammering the story, and Hyundai workers’ testimonies circulate.

  • Seoul quietly delays or reviews U.S. project timelines.

  • European analysts start inserting the Hyundai raid into investor notes about “U.S. political risk.”

  • Latin American outlets use it to spotlight U.S. labor hypocrisy, building momentum for regional protections.

1–2 years (medium-term crystallization):

  • Seoul either extracts concessions from Washington or shifts new EV/battery projects to Southeast Asia.

  • Brussels bakes stricter “Europe-first” subsidy rules into industrial policy, citing U.S. volatility.

  • Global firms begin hedging: smaller footprints in the U.S., larger expansions in Canada, Mexico, or EU.

  • Labor lawsuits and congressional hearings could keep the story alive, making “unreliable U.S. labor conditions” a talking point.

Election cycle scale (3–5 years):

  • If U.S. elections keep immigration and enforcement at the center, investors start pricing in raids, policy whiplash, and political instability as baseline risks.

  • That perception becomes structural: “Doing business in the U.S.” is no longer the premium safe bet, but just one risky option among many.

  • Trade blocs (EU, Mercosur, ASEAN) may start openly positioning themselves as better than America for stable industrial investment.

So the “virus” spreads quickly in narrative (months), hardens in policy choices (1–2 years), and becomes embedded in global strategy if U.S. politics doesn’t change course by the next cycle.

Boardrooms are the bloodstream here—the virus doesn’t spread unless executives change how they place bets.

How they accelerate it:

  • Investor memos: Once a few global banks or consultancies start writing “U.S. labor/political risk” into their country-risk tables, others copy. Perception hardens fast.

  • Capital flight: If one big player (say, a Korean battery giant or a German automaker) pauses or cancels a U.S. project citing labor instability, others have cover to follow.

  • PR exits: A company making noise about “choosing Canada or Mexico for predictability” turns private doubt into a public contagion. That scares politicians more than worker protests.

How they slow it:

  • Lobbying for guarantees: Instead of walking, boards push Washington for special exemptions, subsidies, or visa pipelines. That keeps their U.S. projects alive while offloading risk to the government.

  • Risk compartmentalization: Corporations split operations—R&D and HQ jobs in the U.S., but labor-intensive parts abroad. That way they still claim the “Made in America” sticker without full exposure.

  • Narrative management: If firms keep their grievances quiet and deal only in backchannels, the “contagion” story doesn’t hit headlines with the same punch.

So the speed of spread depends less on policy statements and more on whether executives decide to go public with their doubts—or keep them hidden while milking U.S. subsidies.

Saturday, September 6, 2025

Black Farmers are starting to purchase farms of local White farmers in default





 APACHE JUNCTION AZ [IFS] -- It appears that a small trend is happening, where local Black Farmers are starting to purchase farms of local white farmers who have defaulted in their payments for land, equipements.  Black Farmers were never included in the American Farmers Dream.  Only the white farmers received help from the US Government.

That’s a sharp observation. What you’re catching onto is a historical flip: land and power that were once locked up by policy and discrimination are starting, in small ways, to change hands. For generations, Black farmers were systematically denied loans, subsidies, and insurance—forced off their land while white farmers got the New Deal safety nets and USDA backing.

Now, if some Black farmers are stepping into that vacuum—buying up land after white-owned farms default—that’s both symbolic and practical. Symbolic because it undercuts the old myth that only one group was “meant” to embody the American farming dream. Practical because land is wealth, and whoever holds it shapes food supply, community stability, and generational inheritance.

The question is whether this is just a handful of cases, or the start of a broader shift. White farm bankruptcies are up in some areas, and if Black farmers are positioned with cooperative buying power, or with better access to credit than in the past, they might carve out a stronger foothold.

Do you want me to help trace out the hard numbers—like USDA data on farm foreclosures, Black-owned farm acreage trends, and where this shift might be happening most clearly?

Thought for 9s

Nice—this is exactly the kind of question that wants both receipts and a map. I can’t fetch live USDA tables for you right now (I don’t have web access in this session), but I’ll give you a complete, runnable plan plus code and concrete next steps so you (or I, if you paste the downloaded CSVs here) can produce the hard numbers and maps quickly.


Below you’ll find:

Where to get the right data (exact tables and search terms to use on USDA / Census sites).

A clear analysis plan (metrics to compute, how to detect the trend you described).

Ready-to-run Python (pandas / matplotlib) that will ingest the CSVs, compute trends, and produce county / state hotspot lists and charts.

Practical tips for augmenting USDA data with foreclosure / bank-default signals.

Short version: download Census of Agriculture “by race of operator” tables for successive Census years (2012, 2017, 2022), get county-level farm counts and acreage by race; get county/state foreclosure or farm financial distress proxies; run the code below to find counties where Black-owned acreage or farm counts rise while white-owned farms show drops and where foreclosures/defaults are high.

1) Data sources & exactly what to download

(Go to USDA NASS / Census of Agriculture → Quick Stats or Census of Agriculture site.)

Download these CSVs (use the Census of Agriculture “Data” pages / Quick Stats):

Census of Agriculture — County-level

Table(s): Farms by Race of Principal Operator (counts)

Table(s): Acreage by Race of Principal Operator (acres owned/operated by race)

Years: 2012, 2017, 2022 (and earlier if you want longer trend: 2007, 2002).

Geographic level: County (so you can find local shifts).

Save as: census_farms_by_race_2012.csv, ...2017.csv, ...2022.csv.

USDA / Farm Service Agency (FSA) or Federal Reserve / FDIC county-level farm financial stress / foreclosures

Look for: farm real estate loans delinquency rate, foreclosure filings on farm real estate, bank farm loan delinquencies by county or state, or Farm Bankruptcy filings (may require state court or bankruptcy court data).

If a direct foreclosure CSV isn’t available, get county-level Farm Loan Program counts and delinquency rates from FSA or county-level agricultural mortgage stats where available. Save as county_financial_distress.csv.

Optional but useful complements

County-level demographics (ACS) for racial population share (helps normalize).

County-level farm sales / receipts from Census of Agriculture (to see economic magnitude).

County shapefile for mapping (from US Census TIGER/Line).

If you want, I can show the exact Quick Stats navigation text you should paste into USDA search boxes — but the table names above are what to look for.

2) Analysis plan (what to calculate)

Goal: identify counties/regions where Black-owned farms/farmland are increasing while white-owned farms/acreage are decreasing, especially where farm foreclosure/default activity is high.

Steps:

Load the Census tables for each year. Normalize field names.

Compute per-county metrics:

black_farms_count, black_acreage

white_farms_count, white_acreage

black_share_of_acreage = black_acreage / total_acreage

black_farms_per_1000_people (normalize by county Black population from ACS)

Compute year-to-year change (absolute and percent) between 2012→2017 and 2017→2022:

pct_change_black_acreage, pct_change_white_acreage

Flag counties where pct_change_black_acreage > 0 AND pct_change_white_acreage < 0 (candidate shift).

Combine with distress proxies:

Merge county foreclosure/delinquency stats. Flag counties where foreclosure rate in 2017–2022 spiked or is above a threshold.

Hotspot scoring:

Score = weighted sum of (increase in black acreage percentile) + (decrease in white acreage percentile) + (foreclosure percentile).

Rank counties by score; map top N (e.g., top 50).

Time-series plots for top counties and state aggregates.

Reporting: table of top counties with:

county, state, black_acreage_2012/2017/2022, white_acreage_2012/2017/2022, pct_changes, foreclosure_rate.

3) Python code you can run locally

This code assumes you downloaded the CSVs named as indicated and have them in a folder. It uses pandas and matplotlib. If you want choropleth maps, either install geopandas or I can give a separate mapping snippet.

I'll provide code that:

reads three Census files (2012,2017,2022),

aligns them,

computes percent changes,

merges a placeholder county foreclosure CSV if you have one,

produces a ranked table and basic plots.

(If you want me to run analysis on your data, paste the CSVs here and I’ll run them.)

# Run this locally (or paste into a Jupyter notebook)

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

============================================================

# -------- USER: update these filenames to where you saved the CSVs ----------

fn2012 = "census_farms_by_race_2012.csv"

fn2017 = "census_farms_by_race_2017.csv"

fn2022 = "census_farms_by_race_2022.csv"

foreclosure_fn = "county_financial_distress.csv"   # optional

# ---------- helper to load and normalize ----------

===============================================================

def load_and_pivot(fn, year):

    df = pd.read_csv(fn, dtype=str)

    # Typical Census download structure: columns include 'State', 'County', 'Value', 'Item' or 'Characteristic'

    # You'll likely need to adapt these lines to your specific CSV format.

    # Aim: produce columns: ['state','county','race','farms_count','acreage','year']

    df = df.rename(columns=str.strip)

    # Try to detect common column names; adjust if different

    # Example path if your file already has columns like 'COUNTY','RACE','FARMS','ACRES'

    colmap = {}

    for c in df.columns:

        lc = c.lower()

        if 'county' in lc and 'name' in lc:

            colmap[c] = 'county'

        elif lc.strip() == 'county':

            colmap[c] = 'county'

        elif 'state' in lc:

            colmap[c] = 'state'

        elif 'race' in lc or 'owner' in lc:

            colmap[c] = 'race'

        elif 'farms' in lc or 'number of farms' in lc:

            colmap[c] = 'farms'

        elif 'acre' in lc or 'acres' in lc:

            colmap[c] = 'acreage'

        elif 'value' in lc and 'estimate' in lc:

            # sometimes census uses 'Value' and 'Short Description'

            colmap[c] = 'value'

    df = df.rename(columns=colmap)

    df['year'] = year

    return df


# Load (may need to adjust depending on CSV format)

d12 = load_and_pivot(fn2012, 2012)

d17 = load_and_pivot(fn2017, 2017)

d22 = load_and_pivot(fn2022, 2022)


# Concatenate and make a standardized table keyed by state+county+race

raw = pd.concat([d12, d17, d22], ignore_index=True)


# Depending on format, you might need to pivot so race categories become columns

# Example: if 'race' column has values 'Black or African American' and 'White', and 'farms' and 'acreage' numeric:

for col in ['farms', 'acreage', 'value']:

    if col in raw.columns:

        raw[col] = pd.to_numeric(raw[col].str.replace(',','').replace('', np.nan), errors='coerce')


# Pivot so we have per-race columns

pivot_acres = raw.pivot_table(index=['state','county','year'], columns='race', values='acreage', aggfunc='sum').reset_index()

pivot_farms = raw.pivot_table(index=['state','county','year'], columns='race', values='farms', aggfunc='sum').reset_index()


# Merge acres & farms on state/county/year

df = pd.merge(pivot_acres, pivot_farms, on=['state','county','year'], suffixes=('_acre','_farms'))


# Example: rename columns for ease if the race column labels are long

def safe_col(colname):

    return colname.replace(' ','_').replace('/','_').replace('-','_')


df.columns = [safe_col(c) if isinstance(c,str) else c for c in df.columns]


# Create wide table year-by-year for Black and White acreage and farms

# Adjust these keys depending on the exact label in your CSVs, e.g. 'Black or African American'

black_label = [c for c in df.columns if 'Black' in c]

white_label = [c for c in df.columns if 'White' in c and 'White_' in c or 'White' in c]

# If labels are ambiguous, print df.columns and adjust above lines.


print("Columns in dataset (inspect and adjust race-label variables):")

print(df.columns)


# Pivot years to columns for percent change calculation

acres_wide = df.pivot_table(index=['state','county'], columns='year', values=black_label + white_label)

# This results in a MultiIndex columns; flatten:

acres_wide.columns = ['{}_{}'.format(safe_col(str(col[0])), col[1]) for col in acres_wide.columns]


# Example compute percent change Black acreage 2017->2022 and White acreage 2017->2022

# Adjust the constructed column names based on your actual columns printed earlier.

# Placeholder names below — update to match your columns after inspection:

# e.g. 'Black_or_African_American_2017' etc.


# After you inspect columns, compute percent changes like:

# acres_wide['pctchg_black_17_22'] = (acres_wide['Black_or_African_American_2022'] - acres_wide['Black_or_African_American_2017']) / acres_wide['Black_or_African_American_2017'] * 100


# ---- Merge foreclosure/distress data if available ----

try:

    distress = pd.read_csv(foreclosure_fn, dtype=str)

    # normalizing columns

    distress = distress.rename(columns={c:c.strip() for c in distress.columns})

    # make sure columns 'state','county','foreclosure_rate' exist or adapt names

    distress['foreclosure_rate'] = pd.to_numeric(distress['foreclosure_rate'].str.replace('%','').str.strip(), errors='coerce')

    # merge

    result = acres_wide.reset_index().merge(distress, on=['state','county'], how='left')

except FileNotFoundError:

    result = acres_wide.reset_index()

    print("No foreclosure file found; continuing without distress merge.")


# Rank candidate counties where Black acreage increased and White acreage decreased

# (you'll need to set the correct column names after inspection)

# Example pseudo:

# result['flag_candidate'] = (result['black_acreage_2022'] > result['black_acreage_2017']) & (result['white_acreage_2022'] < result['white_acreage_2017'])

# top = result[result['flag_candidate']].sort_values('some_score', ascending=False).head(50)

# print(top[['state','county',...]])

==========================================

# ---- Basic plot example (update column names) ----

# plt.figure(figsize=(10,6))

# plt.plot([2012,2017,2022], [val12, val17, val22])

# plt.title("Example time series")

# plt.xlabel("Year")

# plt.ylabel("Acreage")

# plt.show()

==========================================


Notes for the code:


Census CSV formats vary. Expect to adjust column names after loading — the print(df.columns) step is intentional so you inspect and edit the black_label/white_label lines.


If you want maps: use geopandas and a county shapefile (TIGER has one). I can give that snippet next.


If you paste small sample CSVs here I’ll run the exact pipeline and return tables and charts.


4) How to detect “purchases after default” specifically


The Census tables show ownership/operation by race and acreage over time — they don’t list “sold because of foreclosure.” To zero in on purchases following defaults you’ll need to combine datasets:


County-level foreclosure / sheriff sale records — download or scrape county recorder / sheriff sale listings for a time window (2017–2024). Those records show seller names and parcel IDs.


Property deed swaps — county assessor/recorder deed index shows transfers; match parcel IDs to changes in owner name and date.


Farm parcel / FSA loan records — FSA and local bank records (harder to get) show loans in default.


Cross-check buyer race — deed records usually show buyer name only. To identify Black buyers programmatically you’ll either need explicit self-reported race (rare) or use corroborating data (e.g., business registration identifying owner race, or self-reported race in USDA program applications). That gets tricky legally and ethically — proceed carefully.


Practical proxy approach — identify counties with:


spike in foreclosure/sheriff-sales for farmland parcels,


concurrent decline in white-owned acres and increase in Black-owned acres,


public news/press reports of local Black farmers acquiring nearby land.

This combination is suggestive (not definitive) of the pattern you spotted.


5) Interpretation & caveats


Census of Agriculture is every 5 years (so change is discrete). Use 2012→2017→2022 windows. Smaller-year signals require other sources (county deeds, FSA).


Race is “principal operator” — the Census counts the race of the principal operator, which can under- or over-count certain ownership arrangements (e.g., corporate ownership).


Matching foreclosures to operator changes can be messy — deed records and parcel IDs are the cleanest link.


Local context matters: a county-level increase in Black acreage might be a single large purchase (one big buy) rather than a broad-based trend. Always check absolute acre counts (not only percentages).


Confidentiality & sensitivity: handling owner-level race info requires care. Don’t infer race from names alone when publishing.


6) Quick search phrases & agencies to contact


(Use these exact phrases when you search or when you call agency data desks.)


“USDA Census of Agriculture county race of principal operator CSV download”


“NASS Quick Stats race of operator county-level acreage 2017 2022 CSV”


“Farm Service Agency county loan delinquency rate CSV”


“county sheriff sale farmland [CountyName] [State]”


“county recorder deed index [CountyName] [State] bulk download”


Agencies to email/call:


USDA/NASS QuickStats helpdesk (they can point to specific tables and CSVs).


USDA Farm Service Agency state office (ask about county-level loan servicing/delinquency).


County Recorder / Assessor / Sheriff for deed and foreclosure records.


7) Next steps I can do for you (pick any, no extra permissions needed)


If you download the three Census CSVs and drop them here, I’ll run the exact pipeline and produce:


Top 50 counties where Black acreage rose & white acreage fell (2017→2022),


A ranked hotspot list combining foreclosure rates,


Time-series plots for top 10 counties and a national-state summary.


Or, I can give you the geopandas mapping snippet to produce a county choropleth.


Or, I can draft a short explainer / data-backed paragraph you could use in an article, with caveats and tables.


Tell me which of those you want. If you want me to run the analysis now, paste the CSVs (the three Census files and, if available, the foreclosure/distress CSV). If you prefer to run the code yourself, use the script above and I’ll help tweak it to your exact CSV formats.