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fredapi is a Python API for the FRED data provided by the Federal Reserve Bank of St. Louis. fredapi provides a wrapper in python to the FRED web service, and also provides several convenient methods for parsing and analyzing point-in-time data (i.e. historic data revisions) from ALFRED

fredapi makes use of pandas and returns data to you in a pandas Series or DataFrame

pip install fredapi

First you need an API key, you can apply for one for free on the FRED website. Once you have your API key, you can set it in one of three ways:

• set it to the evironment variable FRED_API_KEY
• save it to a file and use the 'api_key_file' parameter
• pass it directly as the 'api_key' parameter

from fredapi import Fred
fred = Fred(api_key='insert api key here')
data = fred.get_series('SP500')

Many economic data series contain frequent revisions. fredapi provides several convenient methods for handling data revisions and answering the quesion of what-data-was-known-when.

In ALFRED there is the concept of a vintage date. Basically every observation can have three dates associated with it: date, realtime_start and realtime_end.

• date: the date the value is for
• realtime_start: the first date the value is valid
• realitime_end: the last date the value is valid

For instance, there has been three observations (data points) for the GDP of 2014 Q1:

<observation realtime_start="2014年04月30日" realtime_end="2014年05月28日" date="2014年01月01日" value="17149.6"/>
<observation realtime_start="2014年05月29日" realtime_end="2014年06月24日" date="2014年01月01日" value="17101.3"/>
<observation realtime_start="2014年06月25日" realtime_end="2014年07月29日" date="2014年01月01日" value="17016.0"/>

This means the GDP value for Q1 2014 has been released three times. First release was on 4/30/2014 for a value of 17149.6, and then there have been two revisions on 5/29/2014 and 6/25/2014 for revised values of 17101.3 and 17016.0, respectively.

data = fred.get_series_first_release('GDP')
data.tail()

this outputs:

date
2013年04月01日 16633.4
2013年07月01日 16857.6
2013年10月01日 17102.5
2014年01月01日 17149.6
2014年04月01日 17294.7
Name: value, dtype: object

Note that this is the same as simply calling get_series()

data = fred.get_series_latest_release('GDP')
data.tail()

this outputs:

2013年04月01日 16619.2
2013年07月01日 16872.3
2013年10月01日 17078.3
2014年01月01日 17044.0
2014年04月01日 17294.7
dtype: float64

fred.get_series_as_of_date('GDP', '6/1/2014')

this outputs:

This returns a DataFrame with all the data from ALFRED

df = fred.get_series_all_releases('GDP')
df.tail()

this outputs:

from __future__ import print_function
vintage_dates = fred.get_series_vintage_dates('GDP')
for dt in vintage_dates[-5:]:
 print(dt.strftime('%Y-%m-%d'))

this outputs:

2014年03月27日
2014年04月30日
2014年05月29日
2014年06月25日
2014年07月30日

You can always search for data series on the FRED website. But sometimes it can be more convenient to search programmatically. fredapi provides a search() method that does a fulltext search and returns a DataFrame of results.

fred.search('potential gdp').T

this outputs:

• pandas

• I have a blog post with more examples written in an IPython notebook