Revised 23 October 2026 · v1.0.0 · what changed

Code: github.com/herrrickshaw/india-trade-export-analysis

Watch the walkthrough

Watch on YouTube: youtu.be/_tfs7QhKoTc

Why it is useful

• Who benefits: students and trade analysts who want a ready commodity-level export table (HS code, 2020 value, 2023 value, growth) without downloading TradeStat themselves.
• What it replaces: the first day of cleaning TradeStat spreadsheets. The results are already in CSV and JSON.
• Evidence from our run: we loaded the committed 14,980-row CSV in pandas and recovered the headline totals, US$291.8 bn in 2020 and US$437.7 bn in 2023.
• Evidence of limits: the line items sum to US$460.5 bn, about 5% more than the total row, so the table is not a clean partition of exports.
• The scripts need raw TradeStat Excel files that are not in the repository, so they could not be re-run.

What it is for

The repo's Python scripts read TradeStat Excel exports, compute growth between 2020 and 2023, project 2025 and 2026 by extending that growth, and classify commodities as mega-growth, high-value growing or emerging opportunity. Markdown write-ups discuss reserves, tax and debt. The README describes it as indicative analysis from public sources, not official statistics.

This guide shows how to explore the committed outputs. It is trade analysis for learning, not investment or business advice. The repository is archived (read-only).

Who it is for

• Students of international trade and Indian export structure
• Analysts who want a starting commodity table to verify against the official source
• Anyone learning how a growth-and-projection screen is built, and where it breaks

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/india-trade-export-analysis.git
cd india-trade-export-analysis
claude      # or: gemini

Install

1. Get the code

   git clone https://github.com/herrrickshaw/india-trade-export-analysis.git
   cd india-trade-export-analysis

2. Make a virtual environment and install pandas

   python3 -m venv .venv
   .venv/bin/pip install pandas

Quick start: commands and what you should see

Load the extended table and read the totals

.venv/bin/python -c "import pandas as pd; d=pd.read_csv('EXTENDED_ANALYSIS_2020_2026.csv'); print(d.shape); print(d.iloc[0])"

14,980 rows and 7 columns; the first row is India's total export with values for 2020 and 2023 in US$ million.

Count the opportunity classes

.venv/bin/python -c "import pandas as pd; print(pd.read_csv('HIGH_OPPORTUNITY_EXPORTS.csv').Opportunity.value_counts())"

Three classes with 246, 41 and 6 commodities.

Run a script (needs raw data)

.venv/bin/python export_opportunities.py

Without the TradeStat workbooks and Downloads folder it cannot run. We did not run it.

Sample outputs

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

Totals versus the sum of line items

python3 -I (pandas, EXTENDED_ANALYSIS_2020_2026.csv)
total row 2020: 291808.48  2023: 437704.58 (USD mn)
sum of 14,979 line items, 2023: 460,482.7 (USD mn)
total row 2023: 437,704.6 (USD mn)

The total row and the line items disagree by about US$22.8 bn (5%). Do not add the total row to the line items.

Opportunity classes

HIGH_OPPORTUNITY_EXPORTS.csv, value_counts of Opportunity
Opportunity
EMERGING_OPPORTUNITY    246
HIGH_VALUE_GROWING       41
MEGA_GROWTH               6
(293 rows)

The 293 rows include the all-India total row.

The six mega-growth lines (first five shown)

MEGA_GROWTH_EXPORTS.csv, values rounded
Commodity (HS code)                      2020 $M   2023 $M   growth
Automotive diesel (27101944)               8,800.8  19,455.9   121.1%
Aviation turbine fuel (27101939)             430.1  15,202.7  3435.0%
Motor gasoline (27101241)                  3,645.8  10,502.9   188.1%
Aeroplanes >15,000 kg (88024000)             115.8   5,544.5  4689.3%
Motor cars 1000-1500cc (87032291)          1,983.8   5,098.6   157.0%

Aviation fuel (+3,435%) and aircraft (+4,689%) start from very small 2020 bases; a change in HS classification or one large delivery could explain this, which the repo does not test. The column is labelled 5-year growth but compares 2020 with 2023.

Ways to use it

Sample prompts for your AI assistant

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

• Read README.md and export_opportunities.py and explain how a commodity becomes MEGA_GROWTH.
• Load EXTENDED_ANALYSIS_2020_2026.csv, drop the total row, and list the top 10 HS codes by 2023 value.
• Check whether the large growth rates in HIGH_OPPORTUNITY_EXPORTS.csv come from tiny 2020 bases and flag them.
• Explain why the line items sum to more than the India's Total Export row and suggest a fix.
• Adapt export_extended_analysis.py to take a folder path as an argument instead of ~/Downloads.

Limits and honest caveats

Sources: Repository herrrickshaw/india-trade-export-analysis (archived), committed CSV/JSON files read with pandas on 2026-10-23 in a throwaway virtual environment; the Python scripts were not run because their input workbooks are absent.

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.