pystlouisfed 2.1.0

Federal Reserve Bank of St. Louis - FRED, ALFRED, GeoFRED and FRASER

Python client for Federal Reserve Bank of St. Louis

Description

This is a third-party client that is developed and maintained independently of the Federal Reserve Bank. As such, it is not affiliated with or supported by the institution.

The Federal Reserve Bank of St. Louis is one of 12 regional Reserve Banks that, along with the Board of Governors in Washington, D.C., make up the United States' central bank. The https://stlouisfed.org site currently provides more than 816,000 time series from 107 sources using the FRED (Federal Reserve Economic Data) and ALFRED (Archival FRED) interfaces. It is also possible to obtain detailed geographical data from GeoFRED ( Geographical Economic FRED) or more than 500,000 publications from the digital library FRASER.

The pystlouisfed package covers the entire FRED / ALFRED / GeoFRED / FRASER API and returns most of the results as pandas.DataFrame, which is cast to the correct data types with a specific index. So "date", "realtime_start", "observation_start" etc are datetime64 type, "value" is float and not str, missing values are np.NaN and not "." etc ... The naming convention of methods and parameters is the same as in the target API and everything is detailed documented. There is also a default rate-limiter, which ensures that the API call limit is not exceeded.

Getting Started

Installing

pip install pystlouisfed

Dependencies

• pandas for time series data and lists
• requests for API calls
• shapely for geometric data from GeoFRED
• sickle for FRASER oai-pmh API
• ratelimiter for limiting API calls

Usage

First you need to register and create an API key.

Documentation

The documentation contains a description of all methods, enums, classes and API calls with individual examples and their results. Or you can display a detailed description directly with the help function.

For example:

from pystlouisfed import FRED

help(FRED.series_search)

Let 's start with FRED and ALFRED

Most FRED (ALFRED) API calls return a list of objects (pandas.DataFrame), but there are a few exceptions. A few methods do not return a pandas.DataFrame, but only one specific object from the pystlouisfed.models.

For example:

"Hey FRED give me Category with ID 125"

from pystlouisfed import FRED

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
category = fred.category(category_id=125)
# Category(id=125, name='Trade Balance', parent_id=13)

or Source with ID 1

from pystlouisfed import FRED

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
fred.source(source_id=1)
# Source(id=1, realtime_start='2022-01-14', realtime_end='2022-01-14', name='Board of Governors of the Federal Reserve System (US)', link='http://www.federalreserve.gov/')

other methods return pandas.DataFrame For example method FRED.category_series (all series for a specific category)

from pystlouisfed import FRED

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
df = fred.category_series(category_id=125).head()

print(df.head())

        realtime_start realtime_end                                              title observation_start observation_end  frequency frequency_short                units units_short      seasonal_adjustment seasonal_adjustment_short              last_updated  popularity  group_popularity                                              notes
id                                                                                                                                                                                                                                                                                                                                                
AITGCBN     2022-02-05   2022-02-05  Advance U.S. International Trade in Goods: Bal...        2021-12-01      2021-12-01    Monthly               M  Millions of Dollars   Mil. of $  Not Seasonally Adjusted                       NSA 2022-01-26 13:31:05+00:00           3                26  This advance estimate represents the current m...
AITGCBS     2022-02-05   2022-02-05  Advance U.S. International Trade in Goods: Bal...        2021-12-01      2021-12-01    Monthly               M  Millions of Dollars   Mil. of $      Seasonally Adjusted                        SA 2022-01-26 13:31:02+00:00          26                26  This advance estimate represents the current m...
BOPBCA      2022-02-05   2022-02-05          Balance on Current Account (DISCONTINUED)        1960-01-01      2014-01-01  Quarterly               Q  Billions of Dollars   Bil. of $      Seasonally Adjusted                        SA 2014-06-18 13:41:28+00:00          10                11  This series has been discontinued as a result ...
BOPBCAA     2022-02-05   2022-02-05          Balance on Current Account (DISCONTINUED)        1960-01-01      2013-01-01     Annual               A  Billions of Dollars   Bil. of $  Not Seasonally Adjusted                       NSA 2014-06-18 13:41:28+00:00           2                11  This series has been discontinued as a result ...
BOPBCAN     2022-02-05   2022-02-05          Balance on Current Account (DISCONTINUED)        1960-01-01      2014-01-01  Quarterly               Q  Billions of Dollars   Bil. of $  Not Seasonally Adjusted                       NSA 2014-06-18 13:41:28+00:00           1                11  This series has been discontinued as a result ...

or method FRED.series_search (search series by text)

from pystlouisfed import FRED

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
df = fred.series_search(search_text='monetary service index')

print(df.head())

        realtime_start realtime_end                                            title observation_start observation_end frequency frequency_short                units units_short  seasonal_adjustment seasonal_adjustment_short              last_updated  popularity  group_popularity                                              notes
id                                                                                                                                                                                                                                                                                                                                         
MSIMZMP     2022-02-05   2022-02-05         Monetary Services Index: MZM (preferred)        1967-01-01      2013-12-01   Monthly               M  Billions of Dollars   Bil. of $  Seasonally Adjusted                        SA 2014-01-17 13:16:42+00:00          20                20  The MSI measure the flow of monetary services ...
MSIM2       2022-02-05   2022-02-05          Monetary Services Index: M2 (preferred)        1967-01-01      2013-12-01   Monthly               M  Billions of Dollars   Bil. of $  Seasonally Adjusted                        SA 2014-01-17 13:16:44+00:00          16                16  The MSI measure the flow of monetary services ...
MSIALLP     2022-02-05   2022-02-05  Monetary Services Index: ALL Assets (preferred)        1967-01-01      2013-12-01   Monthly               M  Billions of Dollars   Bil. of $  Seasonally Adjusted                        SA 2014-01-17 13:16:45+00:00          14                14  The MSI measure the flow of monetary services ...
MSIM1P      2022-02-05   2022-02-05          Monetary Services Index: M1 (preferred)        1967-01-01      2013-12-01   Monthly               M  Billions of Dollars   Bil. of $  Seasonally Adjusted                        SA 2014-01-17 13:16:45+00:00           9                 9  The MSI measure the flow of monetary services ...
MSIM2A      2022-02-05   2022-02-05        Monetary Services Index: M2 (alternative)        1967-01-01      2013-12-01   Monthly               M  Billions of Dollars   Bil. of $  Seasonally Adjusted                        SA 2014-01-17 13:16:44+00:00           8                 8  The MSI measure the flow of monetary services ...

or method FRED.series_observations (observations for specific series ID)

from matplotlib import pyplot as plt
from pystlouisfed import FRED

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
# T10Y2Y  -  10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity
df = fred.series_observations(series_id='T10Y2Y')

df.plot(y='value', grid=True)
plt.show()

In addition, each DataFrame has correctly set data types.

from pystlouisfed import FRED

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
df = fred.category_series(125)

print(df.info(verbose=True, memory_usage='deep'))

<class 'pandas.core.frame.DataFrame'>
Index: 47 entries, AITGCBN to IEABCSN
Data columns (total 15 columns):
 #   Column                     Non-Null Count  Dtype              
---  ------                     --------------  -----              
 0   realtime_start             47 non-null     datetime64[ns]     
 1   realtime_end               47 non-null     datetime64[ns]     
 2   title                      47 non-null     string             
 3   observation_start          47 non-null     datetime64[ns]     
 4   observation_end            47 non-null     datetime64[ns]     
 5   frequency                  47 non-null     category           
 6   frequency_short            47 non-null     category           
 7   units                      47 non-null     category           
 8   units_short                47 non-null     category           
 9   seasonal_adjustment        47 non-null     category           
 10  seasonal_adjustment_short  47 non-null     category           
 11  last_updated               47 non-null     datetime64[ns, UTC]
 12  popularity                 47 non-null     int64              
 13  group_popularity           47 non-null     int64              
 14  notes                      47 non-null     string             
dtypes: category(6), datetime64[ns, UTC](1), datetime64[ns](4), int64(2), string(2)
memory usage: 25.0 KB

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Working with Enums

FRED (ALFRED) has many different parameters, which are not the same for each method. So there is no need to remember everything or keep looking at the documentation. pystlouisfed uses the Enums constants. For example, the API endpoint FRED:series_observations (and method FRED.series_observations) has the optional parameters "units", "frequency", "aggregation_method" or "output_type":

    def series_observations(
            self,
            series_id: str,
            realtime_start: date = date.today(),
            realtime_end: date = date.today(),
            sort_order: enums.SortOrder = enums.SortOrder.asc,
            observation_start: date = date(1776, 7, 4),
            observation_end: date = date(9999, 12, 31),
            units: enums.Unit = enums.Unit.lin,
            frequency: enums.Frequency = None,
            aggregation_method: enums.AggregationMethod = enums.AggregationMethod.average,
            output_type: enums.OutputType = enums.OutputType.realtime_period,
            vintage_dates: List[str] = None
    ) -> pd.DataFram:

But what should be the value? For example, for the parameter "aggregation_method" it is possible to use pystlouisfed.AggregationMethod:

from enum import Enum


class AggregationMethod(Enum):
    """
    A key that indicates the aggregation method used for frequency aggregation.
    """

    avg = 'avg'
    """
    Average (same as `pystlouisfed.enums.AggregationMethod.average`)
    """
    average = 'avg'
    """
    Average (same as `pystlouisfed.enums.AggregationMethod.avg`)
    """
    sum = 'sum'
    """
    Sum
    """
    eop = 'eop'
    """
    End of Period (same as `pystlouisfed.enums.AggregationMethod.end_of_period`)
    """
    end_of_period = 'eop'
    """
    End of Period (same as `pystlouisfed.enums.AggregationMethod.eop`)
    """

The method above can then be called as follows:

from pystlouisfed import FRED, AggregationMethod, Frequency

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
df = fred.series_observations(series_id='T10Y2Y', aggregation_method=AggregationMethod.end_of_period, frequency=Frequency.weekly_ending_friday)

Working with rate limiting

The API is limited to 120 calls per 60 seconds. pystlouisfed therefore, by default uses ratelimiter, which monitors this limit! So it is not a problem to download all series (~800) with the tag "daily" and "nsa" (Not Seasonally Adjusted) without exceeding any limits:

from pystlouisfed import FRED

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
series = fred.tags_series(tag_names=['daily', 'nsa'], exclude_tag_names=['discontinued'])

for series_id in series.index.values:
    df = fred.series_observations(series_id=series_id)

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Working with data revisions

https://fred.stlouisfed.org/docs/api/fred/fred_vs_alfred.html

Most users are interested in FRED and not ALFRED. In other words, most people want to know what's the most accurate information about the past that is available today (FRED) not what information was known on some past date in history (ALFRED®). Note that the FRED and ALFRED web services use the same URLs but with different options. The default options for each URL have been chosen to make the most sense for FRED users. In particular by default, the real-time period has been set to today's date. ALFRED® users can change the real-time period by setting the realtime_start and realtime_end variables.

For example, "GDP" has 303 values for today.

from pystlouisfed import FRED

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
df = fred.series_observations(series_id='GDP')

print(len(df))
# 303

But if we request all the changes, we get 3068 values!

from pystlouisfed import FRED
from datetime import date

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
df = fred.series_observations(series_id='GDP', realtime_start=date(1776, 7, 4))

print(len(df))
# 3068

Of course, it is possible to set the range or only one day (set same date value for realtime_start and realtime_end). Let's say we want all changes between "2021-11-01" and " 2022-01-01":

from pystlouisfed import FRED
from datetime import date

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
df = fred.series_observations(series_id='GDP', realtime_start=date(2021, 11, 1), realtime_end=date(2022, 1, 1))

df.loc['2021-07-01':'2021-07-01']

and we see how the value for day "2021-07-01" has changed.

           realtime_start realtime_end      value
date                                             
2021-07-01     2021-11-01   2021-11-23  23173.496
2021-07-01     2021-11-24   2021-12-21  23187.042
2021-07-01     2021-12-22   2022-01-01  23202.344

Between dates "2021-11-01" - "2021-11-23" was 23173.496, then until "2021-12-21" at 23187.042 and finally at 23202.344. I think this is important information for backtesting. Because the backtest on the current/last data will be wrong.

Many other features in the documentation.

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Working with TimeZones

This functionality is currently on the TODO list.

FRED/ALFRED works with date in 99% of cases. But what is a date? For example, the friday "2022-02-04" can be almost anything - it depends on the time zone:

Why we are interested in this?

Let's say we are in the "Europe/Prague" timezone (UTC+1) and at 2:00am we call the method:

from pystlouisfed import FRED
from datetime import date

fred = FRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
fred.series_observations(series_id='GDP', realtime_start=date.today(), realtime_end=date.today())

FRED/ALFRED will return the error:

"Bad Request. Variable realtime_start can not be after today's date..."

because it works in the timezone "US/Central" (UTC−06:00)! Probably all the date values that the API returns are in "US/Central", but I haven't verified it.

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GeoFRED

https://geofred.stlouisfed.org/about/

GeoFRED® allows you to create, customize, and share geographical maps of data found in FRED®. Easily access the details and adjust how the data are displayed. You can also transform the data and download it according to geographic category and time frame.

For example, the GeoFRED.shapes method returns a list of the pystlouisfed.models.Shape object.

This result can be plotted:

import matplotlib.pyplot as plt
from descartes import PolygonPatch
from pystlouisfed import GeoFRED, ShapeType

plt.figure()
ax = plt.axes()
geo_fred = GeoFRED(api_key='abcdefghijklmnopqrstuvwxyz123456')

for country_shape in geo_fred.shapes(shape=ShapeType.country):
    ax.add_patch(PolygonPatch(country_shape.geometry, ec='#999999', fc='#6699cc', alpha=0.5, zorder=2))

ax.axis('scaled')
plt.show()

Or it is possible to return data for a specific series ID:

from pystlouisfed import GeoFRED, ShapeType

geo_fred = GeoFRED(api_key='abcdefghijklmnopqrstuvwxyz123456')
df = geo_fred.series_data(series_id="WIPCPI")

print(df.head())

       region code    value series_id  year
0     Alabama   01  46479.0    ALPCPI  2020
1      Alaska   02  63502.0    AKPCPI  2020
2     Arizona   04  49648.0    AZPCPI  2020
3    Arkansas   05  47235.0    ARPCPI  2020
4  California   06  70192.0    CAPCPI  2020

Other functions in the documentation.

FRASER

https://fraser.stlouisfed.org/about

FRASER is a digital library of U.S. economic, financial, and banking history—particularly the history of the Federal Reserve System.

Providing economic information and data to the public is an important mission for the St. Louis Fed started by former St. Louis Fed Research Director Homer Jones in 1958. FRASER began as a data preservation and accessibility project of the Federal Reserve Bank of St. Louis in 2004 and now provides access to data and policy documents from the Federal Reserve System and many other institutions.

The Fraser interface communicates using the OAI-PMH API. It is thus possible to obtain metadata about hundreds of thousands publications.

For example:

from pystlouisfed import FRASER

fraser = FRASER()
record = fraser.get_record(identifier='oai:fraser.stlouisfed.org:title:176')
metadata = record.get_metadata()

print(metadata['url'])

[
    'https://fraser.stlouisfed.org/title/investigation-economic-problems-176',
    'https://fraser.stlouisfed.org/images/record-thumbnail.jpg',
    'https://fraser.stlouisfed.org/docs/historical/senate/1933sen_investeconprob/1933sen_investeconprob.pdf'
]

Other functions in the documentation.

License

Distributed under the MIT License. See LICENSE for more information.

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