Copper Price Dataset

A public repository of historical copper price data from the Winding Wires Manufacturers' Association of India (WWMAI).

Description

This repo contains copper price data extracted from official WWMAI price list circulars. The data tracks the indicative base price of 8 mm dia. Electrolytic CC Copper wire Rods along with all the components that contribute to the final price calculation.

Data Structure

The repository is organized as follows:

copper-price-data/
│
├── data/
│   ├── processed/
│   │   └── copper_prices.csv   # Consolidated dataset in CSV format
│   │
│   └── raw/                    # Original PDF circulars
│       └── YYYY/               # Organized by year
│           └── MM-Month/       # Then by month
│               └── YYYY-MM-DD.pdf
│
└── scripts/                    # Utility scripts for processing

CSV Data Format

The dataset (data/processed/copper_prices.csv) contains these fields:

Field	Description
date	Publication date of the price list
reference_number	Document reference number
copper_grade	Specification of the copper product
lme_price_usd_mt	London Metal Exchange price in USD per metric ton
premium_usd_mt	Premium for CC Copper Wire Rods in USD per MT
transaction_cost_usd_mt	Transaction costs in USD per MT
exchange_rate_inr_usd	Exchange rate (INR per USD)
multiplying_factor	Multiplying factor used by WWMAI
freight_handling_inr_kg	Freight and handling charges in INR per kg
base_price_inr_kg	Final calculated base price in INR per kg
valid_from	Start date of price validity
valid_to	End date of price validity
notes	Additional information

Usage

Accessing the Data

The data can be used in various ways:

1. Direct download: Get the CSV file for use in your application
2. Git clone: Clone the entire repository to access both raw PDFs and processed data

   git clone https://github.com/iodize6399/wwmai-copper-data.git
   

3. Staying updated: Pull the latest changes periodically to get the most recent data

Example Usage (Python)

import pandas as pd

# Load the data
copper_data = pd.read_csv('data/processed/copper_prices.csv')

# Convert date columns to datetime
date_columns = ['date', 'valid_from', 'valid_to']
for col in date_columns:
    copper_data[col] = pd.to_datetime(copper_data[col])

# Analyze price trends
monthly_avg = copper_data.groupby(copper_data['date'].dt.strftime('%Y-%m'))[['base_price_inr_kg']].mean()
print(monthly_avg)

Data Extraction Method

The data in this repository has been extracted from PDF documents using Optical Character Recognition (OCR) combined with Large Language Models (LLMs). The automated extraction process might occasionally introduce errors.

Important: Users are encouraged to verify any critical data against the original PDF files provided in the data/raw/ directory. If you find any discrepancies between the CSV data and the source PDFs, please report them by opening an issue on this repository.

Updates

This dataset is updated regularly as new price lists are published. Each update includes:

• The original PDF source files
• Updates to the consolidated CSV

License

This dataset is available under the Creative Commons Attribution 4.0 International License.

Disclaimer

This repository is not officially affiliated with the Winding Wires Manufacturers' Association of India. The data is collected from publicly available circulars for research and informational purposes only. Use at your own risk.