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 FRED Maps or more than 500,000 publications from the digital library FRASER.
The pystlouisfed package covers the entire FRED / ALFRED / FRED Maps / 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.
pip install pystlouisfed
- pandas for time series data and lists
- geopandas for time series data and lists
- requests for API calls
- sickle for FRASER oai-pmh API
- rush for limiting API calls
First you need to register and create an API key.
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)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)
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 ...
Everything can be easily displayed in a graph.
For example FRED.series_observations (observations for specific series ID) can be plotted with default matplotlib
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()
Of course, we can use any library, for example Plotly:
import plotly.express as px
from pystlouisfed import FRED
fred = FRED(api_key='3a3380d8e2f1c64b28f3bb4805ca6c22')
df = fred.series_observations(series_id='SP500')
fig = px.scatter(
x=df.index,
y=df.value,
trendline="ols",
trendline_color_override="red",
title=f"S&P 500",
labels={"x": "Date", "y": "Index"},
)
fig.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
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 = None,
realtime_end: date = None,
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.DataFrame:
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)The API is limited to 120 calls per 60 seconds.
pystlouisfed therefore, by default uses rush, 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)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))
# 303But 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))
# 3068Of 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.
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.
https://fredaccount.stlouisfed.org/public/dashboard/83217
Maps provide a cross-sectional perspective that lets you compare regions on a map while complementing and expanding the data analysis you get on a time-series graph. FRED has 9 types of maps:
- U.S. counties,
- U.S. metro areas,
- U.S. states,
- nations,
- Federal Reserve Districts,
- Census regions,
- Census divisions,
- BEA regions
- NECTAs (New England city and town areas)
For example, the FREDMaps.shapes method returns a geopandas.GeoDataFrame.
This result can be plotted:
import plotly.express as px
from pystlouisfed import FREDMaps, ShapeType
gdf = FREDMaps(api_key="abcdefghijklmnopqrstuvwxyz123456") \
.shapes(shape=ShapeType.country) \
.to_crs(epsg=4326) \
.set_index("name")
fig = px.choropleth(
gdf,
geojson=gdf.geometry,
locations=gdf.index,
color="fips",
)
fig.update_layout(width=1200, height=1000, showlegend=False)
fig.update_geos(fitbounds="locations", visible=False)
fig.show()
Or it is possible to return data for a specific series ID:
from pystlouisfed import FREDMaps
fred_maps = FREDMaps(api_key="abcdefghijklmnopqrstuvwxyz123456")
fred_maps.series_data(series_id='WIPCPI')
print(fred_maps.head())
# region code value series_id year
# 0 Louisiana 22 54622 LAPCPI 2022-01-01
# 1 Nevada 32 61282 NVPCPI 2022-01-01
# 2 Maryland 24 70730 MDPCPI 2022-01-01
# 3 Arizona 4 56667 AZPCPI 2022-01-01
# 4 New York 36 78089 NYPCPI 2022-01-01Other functions in the documentation.
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){
"accessCondition": ["http://rightsstatements.org/vocab/NoC-US/1.0/"],
"classification": ["Y 4.F 49:Ec 7/"],
"contentType": ["title"],
"dateIssued": ["February 13-28, 1933"],
"digitalOrigin": ["reformatted digital"],
"extent": ["1246 pages"],
"form": ["print"],
"genre": ["government publication"],
"geographic": [None, "United States"],
"identifier": ["4350587"],
"internetMediaType": ["application/pdf"],
"issuance": ["monographic"],
"language": ["eng"],
"location": [None],
"name": [None, None],
"originInfo": [None],
"physicalDescription": [None],
"place": ["Washington"],
"publisher": ["Government Printing Office"],
"recordInfo": [None, None, None, None, None, None, None, None],
"relatedItem": [None],
"role": [None, None],
"roleTerm": ["creator", "contributor"],
"sortDate": ["1933-02-13"],
"recordIdentifier": ["524", "8499", "8", "97", "4145", "6824", "4293", "5292"],
"subTitle": ["Hearings Before the Committee on Finance, United States Senate"],
"subject": [None],
"namePart": [
"United States. Congress. Senate. Committee on Finance",
"1815-",
"Seventy-Second Congress",
"1931-1933"
],
"theme": [
None,
"Great Depression",
None,
"Meltzer\"s History of the Federal Reserve - Primary Sources"
],
"title": ["Investigation of Economic Problems", "Congressional Documents"],
"titleInfo": [None, None],
"titlePartNumber": ["Seventy-Second Congress, Second Session, Pursuant to S. Res. 315, February 13 to 28, 1933"],
"topic": [None, "Economic conditions", None, "Congressional hearings"],
"typeOfResource": ["text"],
"url": [
"https://fraser.stlouisfed.org/oai/title/investigation-economic-problems-176",
"https://fraser.stlouisfed.org/images/record-thumbnail.jpg",
"https://fraser.stlouisfed.org/oai/docs/historical/senate/1933sen_investeconprob/1933sen_investeconprob.pdf"
]
}Other functions in the documentation.
Distributed under the MIT License. See LICENSE for more information.