Revised 29 October 2026 · v1.0.0 · what changed

Code: github.com/herrrickshaw/focus-sector-investor-map

Watch the walkthrough

Watch on YouTube: youtu.be/SaLR1QTzqa8

Why it is useful

• Who benefits: policy analysts, investment-promotion teams and researchers who need to know which companies, in which countries, belong to each focus sector without building the sector classification themselves.
• What it replaces: hand-matching a stock universe to sector names, and hunting through press releases to find which foreign company sits behind which incentive scheme.
• Evidence from the run: the country matrix sums to exactly 19,795 companies across 107 countries; Electronics & Semiconductors is the largest sector (3,449 companies, 52 countries) and Shipbuilding & Marine the smallest focus sector (359).
• The pairing file keeps its negative findings (a stalled steel JV, a lapsed battery award, a shrinking beneficiary list), so it is a safer starting point than a list of announcements.

What it is for

The repository joins India's trade-policy focus sectors to the global listed-company universe. You get a coverage layer (every company classified into the twelve sectors), a screened set of liquid names, a shortlist checked for profitability, and a file that pairs foreign investors with the Indian initiative that is their vehicle.

It is a research starting point, not a recommendation. Nothing in the files tells you to buy or sell anything.

Not investment advice.

Who it is for

• Researchers and students of Indian industrial policy
• Investment-promotion and business-development teams looking at which countries to court
• Analysts who want a sector-tagged global company list to filter further

What you need

Get the code and an AI assistant

You do not have to be a programmer. Get the repository, give it to an AI assistant, and ask in plain language. Any of these routes works:

git clone https://github.com/herrrickshaw/focus-sector-investor-map.git
cd focus-sector-investor-map
claude      # or: gemini

Install

1. Clone the repository (needs git)

   git clone https://github.com/herrrickshaw/focus-sector-investor-map.git
   cd focus-sector-investor-map

2. Look at the output files (nothing to install)

   ls data/

Quick start: commands and what you should see

Count companies per focus sector and country from the committed matrix

python3 -I - <<'E'
import csv,collections
tot=collections.Counter();cn=collections.defaultdict(dict)
for r in csv.DictReader(open('data/focus_sector_country_matrix.csv')):
    tot[r['FocusSector']]+=int(r['Companies']);cn[r['FocusSector']][r['Country']]=int(r['Companies'])
for s,t in tot.most_common():
    top=sorted(cn[s].items(),key=lambda x:-x[1])[:3]
    print(s,t,len(cn[s]),top)
E

Twelve lines, one per sector, with the company total, the number of countries and the top three countries. The totals add up to 19,795.

List the investor-to-scheme pairings

python3 -I - <<'E'
import json
d=json.load(open('data/foreign_investor_initiative_pairs.json'))
for k in ('verified_pairs','prospective_pairs'):
    for x in d[k]: print(k[:4],x['company'],'|',x['sector'],'|',x['status'])
E

19 lines: 12 verified and 7 prospective pairings with their status.

Sample outputs

Real output from a run on the date shown in the sources line; trimmed to fit.

Sector by country coverage (data/focus_sector_country_matrix.csv)

sector | companies | countries | top 3 countries
Electronics & Semiconductors | 3449 | 52 | China 837, Taiwan 789, South Korea 487
Specialty Steel & Metals | 2593 | 74 | Canada 833, Australia 515, India 236
Chemicals & Plastics | 2453 | 71 | China 612, India 481, Japan 190
Pharma & Bulk Drugs | 2453 | 62 | United States 724, China 386, South Korea 226
White Goods & Electricals | 2129 | 68 | China 501, United States 249, India 234
Green Energy & Fuels | 1495 | 81 | United States 219, Canada 212, China 148
Medical Devices | 842 | 38 | United States 204, China 138, South Korea 92
Shipbuilding & Marine | 359 | 57 | China 39, India 28, Indonesia 27
Aerospace & Defence | 319 | 31 | United States 79, China 68, India 23
(total across all 12 sectors: 19,795)

Three sectors omitted here to save space: Food Processing 1,450; Textiles & Apparel 1,291; Auto, EV & Components 962. Counts are classification counts, not market size.

Investor-to-initiative pairings (data/foreign_investor_initiative_pairs.json, retrieved 2026-07-20)

veri | Foxconn (Hon Hai) | Electronics & Semiconductors | PRODUCING (PIB-credited)
veri | Micron Technology | Semiconductors | APPROVED/BUILDING
veri | Renesas Electronics | Semiconductors | APPROVED (partner)
veri | LG Electronics | White Goods | PARTICIPANT
veri | POSCO Holdings | Specialty Steel | MoU-STAGE (clearance-proof of stall)
veri | First Solar | Solar Manufacturing | BUILDING/PRODUCING
pros | Applied Materials | Semicon equipment | PROSPECTIVE
pros | Lam Research | Semicon equipment | PROSPECTIVE
pros | Airbus | Aerospace | PRODUCING (defence JV)

Each verified pair cites a Press Information Bureau release number (PRID) or Cabinet approval in the file; the POSCO row is a deliberate negative finding.

Final shortlist: verified-profitable names per sector (aggregate only)

147 names in focus_sector_companies_final.csv, all marked PROFITABLE
102 carry the expansion signal (revenue growth >= 10% or 1-year return >= 25%); 45 do not
per sector: 3 to 8 names per region (India/Foreign), e.g. Pharma & Bulk Drugs: 8 India, 5 Foreign
Medical Devices: 7 Foreign, 0 India

Company-level rows are in the repo; they are not reproduced here because they come from a free but unaudited data source.

Ways to use it

Sample prompts for your AI assistant

Copy, paste and edit the parts in [brackets].

• Claude: Read README.md and data/focus_sector_country_matrix.csv, then tell me which five countries have the most listed companies in [sector].
• Gemini: Open data/foreign_investor_initiative_pairs.json and make a table of verified versus prospective pairs, with the scheme each one uses.
• ChatGPT: Here is focus_sector_companies_final.csv. Which sectors have no India-listed names, and why might that be?
• GitHub (Copilot Chat or Codespaces): Write a pandas script that counts companies by Country for the sector [sector] and saves a bar chart.
• Claude: Explain the difference between the global catalog, the screen and the final list, and how each is filtered.
• Any assistant: List the caveats in the README and tell me which of them would change if I used this for [my purpose].

Limits and honest caveats

Sources: Read the committed CSV and JSON files from a fresh clone of github.com/herrrickshaw/focus-sector-investor-map (commit ba555ba) using the Python standard library on 19 October 2026. Build scripts and the yfinance enrichment were not run (they need local inputs and live calls). Underlying data: Damodaran industry classification, market data from the author's stack, PIB releases via the companion trade-policy repo.

About this article: Researched, written and edited by Umashankar Triplicane Dwarakanathan, with AI research assistance; every figure is meant to trace to the primary source cited. See the Editorial Policy for how sourcing, AI use and corrections work.