fredapi: Python API for FRED (Federal Reserve Economic Data)

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

Installation

pip install fredapi

Basic Usage

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')

Working with data revisions

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="2014-04-30" realtime_end="2014-05-28" date="2014-01-01" value="17149.6"/>
<observation realtime_start="2014-05-29" realtime_end="2014-06-24" date="2014-01-01" value="17101.3"/>
<observation realtime_start="2014-06-25" realtime_end="2014-07-29" date="2014-01-01" 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.

Get first data release only (i.e. ignore revisions)

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

this outputs:

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

Get latest data

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

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

this outputs:

2013-04-01    16619.2
2013-07-01    16872.3
2013-10-01    17078.3
2014-01-01    17044.0
2014-04-01    17294.7
dtype: float64

Get latest data known on a given date

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

this outputs:

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>date</th> <th>realtime_start</th> <th>value</th> </tr> </thead> <tbody> <tr> <th>2237</th> <td> 2013-10-01 00:00:00</td> <td> 2014-01-30 00:00:00</td> <td> 17102.5</td> </tr> <tr> <th>2238</th> <td> 2013-10-01 00:00:00</td> <td> 2014-02-28 00:00:00</td> <td> 17080.7</td> </tr> <tr> <th>2239</th> <td> 2013-10-01 00:00:00</td> <td> 2014-03-27 00:00:00</td> <td> 17089.6</td> </tr> <tr> <th>2241</th> <td> 2014-01-01 00:00:00</td> <td> 2014-04-30 00:00:00</td> <td> 17149.6</td> </tr> <tr> <th>2242</th> <td> 2014-01-01 00:00:00</td> <td> 2014-05-29 00:00:00</td> <td> 17101.3</td> </tr> </tbody> </table>

Get all data release dates

This returns a DataFrame with all the data from ALFRED

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

this outputs:

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>date</th> <th>realtime_start</th> <th>value</th> </tr> </thead> <tbody> <tr> <th>2236</th> <td> 2013-07-01 00:00:00</td> <td> 2014-07-30 00:00:00</td> <td> 16872.3</td> </tr> <tr> <th>2237</th> <td> 2013-10-01 00:00:00</td> <td> 2014-01-30 00:00:00</td> <td> 17102.5</td> </tr> <tr> <th>2238</th> <td> 2013-10-01 00:00:00</td> <td> 2014-02-28 00:00:00</td> <td> 17080.7</td> </tr> <tr> <th>2239</th> <td> 2013-10-01 00:00:00</td> <td> 2014-03-27 00:00:00</td> <td> 17089.6</td> </tr> <tr> <th>2240</th> <td> 2013-10-01 00:00:00</td> <td> 2014-07-30 00:00:00</td> <td> 17078.3</td> </tr> <tr> <th>2241</th> <td> 2014-01-01 00:00:00</td> <td> 2014-04-30 00:00:00</td> <td> 17149.6</td> </tr> <tr> <th>2242</th> <td> 2014-01-01 00:00:00</td> <td> 2014-05-29 00:00:00</td> <td> 17101.3</td> </tr> <tr> <th>2243</th> <td> 2014-01-01 00:00:00</td> <td> 2014-06-25 00:00:00</td> <td> 17016</td> </tr> <tr> <th>2244</th> <td> 2014-01-01 00:00:00</td> <td> 2014-07-30 00:00:00</td> <td> 17044</td> </tr> <tr> <th>2245</th> <td> 2014-04-01 00:00:00</td> <td> 2014-07-30 00:00:00</td> <td> 17294.7</td> </tr> </tbody> </table>

Get all vintage dates

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:

2014-03-27
2014-04-30
2014-05-29
2014-06-25
2014-07-30

Search for data series

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:

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>series id</th> <th>GDPPOT</th> <th>NGDPPOT</th> </tr> </thead> <tbody> <tr> <th>frequency</th> <td>Quarterly</td> <td>Quarterly</td> </tr> <tr> <th>frequency_short</th> <td>Q</td> <td>Q</td> </tr> <tr> <th>id</th> <td>GDPPOT</td> <td>NGDPPOT</td> </tr> <tr> <th>last_updated</th> <td>2014-02-04 10:06:03-06:00</td> <td>2014-02-04 10:06:03-06:00</td> </tr> <tr> <th>notes</th> <td> Real potential GDP is the CBO&#39;s estimate of the output the economy would produce with a high rate of use of its capital and labor resources. The data is adjusted to remove the effects of inflation.</td> <td>None</td> </tr> <tr> <th>observation_end</th> <td>2024-10-01 00:00:00</td> <td>2024-10-01 00:00:00</td> </tr> <tr> <th>observation_start</th> <td>1949-01-01 00:00:00</td> <td>1949-01-01 00:00:00</td> </tr> <tr> <th>popularity</th> <td>72</td> <td>61</td> </tr> <tr> <th>realtime_end</th> <td>2014-08-23 00:00:00</td> <td>2014-08-23 00:00:00</td> </tr> <tr> <th>realtime_start</th> <td>2014-08-23 00:00:00</td> <td>2014-08-23 00:00:00</td> </tr> <tr> <th>seasonal_adjustment</th> <td>Not Seasonally Adjusted</td> <td>Not Seasonally Adjusted</td> </tr> <tr> <th>seasonal_adjustment_short</th> <td>NSA</td> <td>NSA</td> </tr> <tr> <th>title</th> <td>Real Potential Gross Domestic Product</td> <td>Nominal Potential Gross Domestic Product</td> </tr> <tr> <th>units</th> <td>Billions of Chained 2009 Dollars</td> <td>Billions of Dollars</td> </tr> <tr> <th>units_short</th> <td>Bil. of Chn. 2009 &#36;</td> <td>Bil. of &#36;</td> </tr> </tbody> </table>

Dependencies

• pandas

More Examples

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