api-data-fetcher

name	api-data-fetcher
description	Fetch economic data from FRED, World Bank, and other APIs
workflow_stage	data
compatibility	["claude-code","cursor","codex","gemini-cli"]
author	Awesome Econ AI Community
version	1.0.0
tags	["Python","API","FRED","World-Bank","data-collection"]

API Data Fetcher

Purpose

This skill helps economists fetch data from major economic data APIs including FRED (Federal Reserve Economic Data), World Bank, IMF, BLS, and OECD. It generates clean, documented Python code with proper error handling.

When to Use

• Downloading macroeconomic indicators
• Building custom datasets from multiple sources
• Automating data updates for ongoing projects
• Fetching cross-country panel data

Instructions

Step 1: Identify Data Requirements

Ask the user:

1. What data do you need? (GDP, unemployment, inflation, etc.)
2. What time period and frequency?
3. What countries/regions?
4. Preferred output format? (CSV, DataFrame, etc.)

Step 2: Select Appropriate API

Data Type	Best Source	Package
US macro	FRED	fredapi
Global development	World Bank	wbdata
Labor statistics	BLS	bls
Cross-country	OECD	pandasdmx
Financial	Yahoo Finance	yfinance

Step 3: Generate Clean Code

Include:

• API key handling (environment variables)
• Error handling for API failures
• Data cleaning and formatting
• Documentation of series definitions

Example Output

"""
Economic Data Fetcher
=====================
Downloads macroeconomic data from FRED and World Bank APIs.
Requires: fredapi, wbdata, pandas

Setup: Set FRED_API_KEY environment variable
Get a free key from: https://fred.stlouisfed.org/docs/api/api_key.html
"""

import os
import pandas as pd
from datetime import datetime, timedelta
from typing import List, Optional, Dict

# ============================================
# FRED Data Fetcher
# ============================================

def fetch_fred_series(
    series_ids: List[str],
    start_date: str = "2000-01-01",
    end_date: Optional[str] = None,
    api_key: Optional[str] = None
) -> pd.DataFrame:
    """
    Fetch time series data from FRED.
    
    Parameters
    ----------
    series_ids : list of str
        FRED series IDs (e.g., ['GDP', 'UNRATE', 'CPIAUCSL'])
    start_date : str
        Start date in YYYY-MM-DD format
    end_date : str, optional
        End date (defaults to today)
    api_key : str, optional
        FRED API key (defaults to FRED_API_KEY env var)
    
    Returns
    -------
    pd.DataFrame
        DataFrame with date index and series as columns
    
    Example
    -------
    >>> df = fetch_fred_series(['GDP', 'UNRATE'], '2010-01-01')
    """
    try:
        from fredapi import Fred
    except ImportError:
        raise ImportError("Install fredapi: pip install fredapi")
    
    # Get API key
    api_key = api_key or os.environ.get('FRED_API_KEY')
      api_key:
         ValueError(
            
            
            
        )
    
    fred = Fred(api_key=api_key)
    end_date = end_date  datetime.now().strftime()
    
    
    data = {}
     series_id  series_ids:
        :
            series = fred.get_series(
                series_id,
                observation_start=start_date,
                observation_end=end_date
            )
            data[series_id] = series
            ()
         Exception  e:
            ()
    
    
    df = pd.DataFrame(data)
    df.index.name = 
    
     df



FRED_SERIES = {
    
    : ,
    : ,
    : ,
    
    
    : ,
    : ,
    : ,
    
    
    : ,
    : ,
    : ,
    
    
    : ,
    : ,
    : ,
    
    
    : ,
    : ,
}






 () -> pd.DataFrame:
    
    :
         wbdata
     ImportError:
         ImportError()
    
    end_year = end_year  datetime.now().year
    
    all_data = []
    
     indicator_code, indicator_name  indicators.items():
        :
            
            data = wbdata.get_dataframe(
                {indicator_code: indicator_name},
                country=countries,
            )
            data = data.reset_index()
            all_data.append(data)
            ()
            
         Exception  e:
            ()
    
    
     all_data:
        df = all_data[]
         other_df  all_data[:]:
            df = df.merge(other_df, on=[, ], how=)
        
        
        df[] = pd.to_datetime(df[]).dt.year
        df = df[(df[] >= start_year) & (df[] <= end_year)]
        
         df
    
     pd.DataFrame()



WORLD_BANK_INDICATORS = {
    
    : ,
    : ,
    : ,
    
    
    : ,
    : ,
    
    
    : ,
    : ,
    
    
    : ,
    : ,
    
    
    : ,
    : ,
}






 __name__ == :
    
    us_macro = fetch_fred_series(
        series_ids=[, , , ],
        start_date=
    )
    
    ()
    (us_macro.tail())
    
    
    us_macro.to_csv()
    ()
    
    
    indicators = {
        : ,
        : ,
        : 
    }
    
    cross_country = fetch_world_bank_data(
        indicators=indicators,
        countries=[, , , , , , , ],
        start_year=
    )
    
    ()
    (cross_country.head())
    
    
    cross_country.to_csv(, index=)
    ()

Requirements

Python Packages

pip install fredapi wbdata pandas

API Keys

• FRED: Free key from https://fred.stlouisfed.org/docs/api/api_key.html
• World Bank: No key required
• BLS: Free key from https://www.bls.gov/developers/

Set environment variables:

export FRED_API_KEY="your_key_here"

Best Practices

1. Store API keys in environment variables - never hardcode
2. Add rate limiting for bulk downloads
3. Cache data locally to avoid repeated API calls
4. Document series definitions from the source
5. Check for revisions in real-time data

Common Pitfalls

• ❌ Hardcoding API keys in scripts
• ❌ Not handling API rate limits
• ❌ Ignoring data vintages/revisions
• ❌ Mixing data frequencies without proper handling

References

• FRED API Documentation
• World Bank Data API
• QuantEcon: Python Data Sources

Changelog

v1.0.0

• Initial release with FRED and World Bank support