pydeflate

pydeflate is a Python package for converting between current and constant prices, with built-in currency conversion support. It handles inflation/deflation adjustments using data from multiple authoritative sources (IMF, World Bank, OECD DAC) and combines price deflators with exchange rates for accurate cross-country, cross-currency, and cross-time comparisons.

What can pydeflate do?

• Convert current prices to constant prices
• Convert constant prices to current prices
• Convert between currencies in both current and constant prices
• Combine deflation and exchange in a single operation
• Work with multiple data sources (IMF, World Bank, OECD DAC)

Installation

Install pydeflate using pip:

pip install pydeflate --upgrade

Quick Start

Here's a simple example that converts current USD values to constant 2015 EUR:

from pydeflate import imf_gdp_deflate, set_pydeflate_path
import pandas as pd

# Set up cache directory (only needed once per script)
set_pydeflate_path("path/to/data/folder")

# Your data in current USD prices
data = {
    'iso_code': ['FRA', 'USA', 'GTM'],
    'year': [2017, 2018, 2019],
    'value': [50, 100, 200]
}
df = pd.DataFrame(data)

# Convert to constant 2015 EUR
result = imf_gdp_deflate(
    data=df,
    base_year=2015,
    source_currency="USA",  # USD
    target_currency="FRA",  # EUR
    id_column="iso_code",
    value_column="value",
    target_value_column="value_constant"
)

print(result)

Key Features

Multiple Data Sources

Choose from three authoritative sources:

• IMF World Economic Outlook: GDP deflators, CPI (period average and end-of-period), with future estimates
• World Bank: GDP deflators (standard and linked series), CPI, and PPP exchange rates
• OECD DAC: Specialized deflators and exchange rates for development assistance

Flexible Conversions

# Current → Constant prices
imf_gdp_deflate(df, base_year=2015, ...)

# Currency conversion only
imf_exchange(df, source_currency="USA", target_currency="GBR", ...)

# Combined deflation + currency conversion
wb_cpi_deflate(df, base_year=2020, source_currency="LCU", target_currency="USD", ...)

Advanced Features

• Plugin System: Register custom data sources
• Context Management: Multiple independent cache directories
• Schema Validation: Optional data quality checks

Example Use Cases

OECD DAC

Track aid flows in constant prices:

# Convert ODA from current USD to constant 2018 USD using DAC deflators
oecd_dac_deflate(
    data=oda_df,
    base_year=2018,
    source_currency="USA",
    target_currency="USA",
    ...
)

World Bank

Compare GDP across countries and time:

# Convert GDP from local currency to constant 2015 USD
wb_gdp_linked_deflate(
    data=gdp_df,
    base_year=2015,
    source_currency="LCU",  # Local currency units
    target_currency="USA",
    ...
)

IMF

Adjust financial data for inflation:

# Convert revenue from current to constant prices using CPI
imf_cpi_deflate(
    data=revenue_df,
    base_year=2020,
    source_currency="GBR",
    target_currency="GBR",  # Same currency, just adjust for inflation
    ...
)

Why pydeflate?

Why convert to constant prices (and a common currency)?

Most money series mix price changes with quantities. To measure real change, convert nominal values to constant prices (inflation‑adjusted). For cross‑country work, also express everything in a common currency. pydeflate performs both steps—price adjustment and FX conversion—in one, reproducible operation.

What problem does this solve?

• Illusory growth: Nominal increases can be just inflation.
• Cross‑country noise: FX swings obscure real trends.
• Incomparable targets: Budgets/pledges in current prices aren’t comparable across years.

When should you convert to constant prices?

• Analyses spanning >1–2 years or periods with non‑trivial inflation.
• Cross‑country / multi‑currency comparisons (aid, revenues, investment).
• Before computing growth rates, elasticities, or price/volume decompositions.

Choose the right index

• GDP deflator: economy‑wide prices; good for macro aggregates and public spending.
• CPI: household purchasing power; good for wages, transfers, poverty lines.
• OECD DAC deflators: for official development assistance reporting.
• Sector indices (where available) for narrow baskets.

Rule of thumb: macro volumes → GDP deflator; household purchasing power → CPI.

Why use pydeflate?

• Accurate: Uses official data from IMF, World Bank, and OECD
• Simple: Intuitive API with sensible defaults
• Flexible: Multiple sources and deflator types

Next Steps

• Getting Started - Detailed setup and DataFrame requirements
• Deflation Guide - All deflation methods with examples
• Currency Exchange - Currency conversion examples
• Data Sources - When to use IMF vs World Bank vs OECD DAC

Version Note

License

MIT License. See LICENSE for details.

Support

• Issues: GitHub Issues
• Source Code: GitHub Repository
• PyPI: pydeflate on PyPI