I launched a Python client for 6M+ economic datasets (bootstrapped).
I realized getting data from FRED/WorldBank and other sources was a pain for my own trading, so I built a unified API. Just shipped the Python wrapper. Here is the tech stack (Next.js/Python/Redis).
It pulls from trusted sources like FRED, IMF, World Bank, OECD, BLS, and more, delivering data as clean pandas DataFrames with built-in caching, async support, and easy configuration.
Comparison:
Unlike general API wrappers (e.g., fredapi or pandas-datareader), datasetiq unifies multiple sources (FRED + IMF + World Bank + 9+ others) under one simple interface, adds smart caching to avoid rate limits, and focuses on macro/global intelligence with pandas-first design. It's more specialized than broad data tools like yfinance or quandl, but easier to use for time-series heavy workflows.
Quick Example
import datasetiq as iq
# Set your API key (one-time setup)
iq.set_api_key("your_api_key_here")
# Get data as pandas DataFrame
df = iq.get("FRED/CPIAUCSL")
# Display first few rows
print(df.head())
# Basic analysis
latest = df.iloc[-1]
print(f"Latest CPI: {latest['value']} on {latest['date']}")
# Calculate year-over-year inflation
df['yoy_inflation'] = df['value'].pct_change(12) * 100
print(df.tail())
Looking for feedback on the developer experience.
* GitHub: https://github.com/DataSetIQ/datasetiq-python
* PyPI: pip install datasetiq
