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: What data do you need? (GDP, unemployment, inflation, etc.) What time period and frequency? What countries/regions? 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

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FRED Data Fetcher

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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' ) if not api_key : raise ValueError ( "FRED API key required. Set FRED_API_KEY environment variable " "or pass api_key parameter. Get a key at: " "https://fred.stlouisfed.org/docs/api/api_key.html" ) fred = Fred ( api_key = api_key ) end_date = end_date or datetime . now ( ) . strftime ( '%Y-%m-%d' )

Fetch each series

data

{ } for series_id in series_ids : try : series = fred . get_series ( series_id , observation_start = start_date , observation_end = end_date ) data [ series_id ] = series print ( f"✓ Downloaded { series_id } " ) except Exception as e : print ( f"✗ Failed to download { series_id } : { e } " )

Combine into DataFrame

df

pd . DataFrame ( data ) df . index . name = 'date' return df

Common FRED series for economists

FRED_SERIES

{

GDP and Output

'GDP' : 'Gross Domestic Product' , 'GDPC1' : 'Real GDP' , 'GDPPOT' : 'Real Potential GDP' ,

Labor Market

'UNRATE' : 'Unemployment Rate' , 'PAYEMS' : 'Total Nonfarm Payrolls' , 'CIVPART' : 'Labor Force Participation Rate' ,

Prices

'CPIAUCSL' : 'Consumer Price Index' , 'PCEPI' : 'PCE Price Index' , 'CPILFESL' : 'Core CPI' ,

Interest Rates

'FEDFUNDS' : 'Federal Funds Rate' , 'DGS10' : '10-Year Treasury Rate' , 'T10Y2Y' : '10Y-2Y Treasury Spread' ,

Money and Credit

'M2SL' : 'M2 Money Stock' , 'TOTRESNS' : 'Total Reserves' , }

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World Bank Data Fetcher

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def fetch_world_bank_data ( indicators : Dict [ str , str ] , countries : List [ str ] = [ 'USA' , 'GBR' , 'DEU' , 'FRA' , 'JPN' ] , start_year : int = 2000 , end_year : Optional [ int ] = None ) -

pd . DataFrame : """ Fetch indicator data from World Bank. Parameters

indicators : dict Dict mapping indicator codes to names e.g., {'NY.GDP.PCAP.CD': 'gdp_per_capita'} countries : list of str ISO 3-letter country codes start_year : int Start year end_year : int, optional End year (defaults to current year) Returns

pd.DataFrame Panel data with country and year Example

indicators = { ... 'NY.GDP.PCAP.CD': 'gdp_per_capita', ... 'SP.POP.TOTL': 'population' ... } df = fetch_world_bank_data(indicators, ['USA', 'GBR']) """ try : import wbdata except ImportError : raise ImportError ( "Install wbdata: pip install wbdata" ) end_year = end_year or datetime . now ( ) . year all_data = [ ] for indicator_code , indicator_name in indicators . items ( ) : try :

Fetch data

data

wbdata . get_dataframe ( { indicator_code : indicator_name } , country = countries , ) data = data . reset_index ( ) all_data . append ( data ) print ( f"✓ Downloaded { indicator_name } " ) except Exception as e : print ( f"✗ Failed to download { indicator_name } : { e } " )

Merge all indicators

if all_data : df = all_data [ 0 ] for other_df in all_data [ 1 : ] : df = df . merge ( other_df , on = [ 'country' , 'date' ] , how = 'outer' )

Filter years

df [ 'year' ] = pd . to_datetime ( df [ 'date' ] ) . dt . year df = df [ ( df [ 'year' ]

= start_year ) & ( df [ 'year' ] <= end_year ) ] return df return pd . DataFrame ( )

Common World Bank indicators

WORLD_BANK_INDICATORS

{

Income and Growth

'NY.GDP.PCAP.CD' : 'GDP per capita (current US$)' , 'NY.GDP.PCAP.KD.ZG' : 'GDP per capita growth (%)' , 'NY.GDP.MKTP.KD.ZG' : 'GDP growth (%)' ,

Population

'SP.POP.TOTL' : 'Population, total' , 'SP.URB.TOTL.IN.ZS' : 'Urban population (%)' ,

Trade

'NE.TRD.GNFS.ZS' : 'Trade (% of GDP)' , 'BX.KLT.DINV.WD.GD.ZS' : 'FDI, net inflows (% of GDP)' ,

Human Capital

'SE.XPD.TOTL.GD.ZS' : 'Education expenditure (% of GDP)' , 'SH.XPD.CHEX.GD.ZS' : 'Health expenditure (% of GDP)' ,

Inequality

'SI.POV.GINI' : 'Gini index' , 'SI.POV.DDAY' : 'Poverty headcount ratio ($1.90/day)' , }

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Usage Example

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if name == "main" :

Example 1: Fetch US macro data from FRED

us_macro

fetch_fred_series ( series_ids = [ 'GDP' , 'UNRATE' , 'CPIAUCSL' , 'FEDFUNDS' ] , start_date = '2010-01-01' ) print ( "\nUS Macro Data (FRED):" ) print ( us_macro . tail ( ) )

Save to CSV

us_macro . to_csv ( 'data/us_macro_fred.csv' ) print ( "\nSaved to data/us_macro_fred.csv" )

Example 2: Fetch cross-country data from World Bank

indicators

{ 'NY.GDP.PCAP.CD' : 'gdp_per_capita' , 'SP.POP.TOTL' : 'population' , 'NY.GDP.MKTP.KD.ZG' : 'gdp_growth' } cross_country = fetch_world_bank_data ( indicators = indicators , countries = [ 'USA' , 'GBR' , 'DEU' , 'FRA' , 'JPN' , 'CHN' , 'IND' , 'BRA' ] , start_year = 2000 ) print ( "\nCross-Country Data (World Bank):" ) print ( cross_country . head ( 10 ) )

Save to CSV

cross_country

.

to_csv

(

'data/cross_country_wb.csv'

,

index

=

False

)

print

(

"\nSaved to data/cross_country_wb.csv"

)

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 Store API keys in environment variables - never hardcode Add rate limiting for bulk downloads Cache data locally to avoid repeated API calls Document series definitions from the source 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