series_id large_stringlengths 3 17 | date date32 | value float64 -3.7 6.48B |
|---|---|---|
A006RE1Q156NBEA | 2026-01-01 | 17.9 |
A006RE1Q156NBEA | 2026-04-01 | 17.9 |
A006RE1Q156NBEA | 2026-07-01 | 18.6 |
A006RY2Q224SBEA | 2026-01-01 | 1.25 |
A006RY2Q224SBEA | 2026-04-01 | 0.82 |
A006RY2Q224SBEA | 2026-07-01 | 3.27 |
A007RE1Q156NBEA | 2026-01-01 | 18.1 |
A007RE1Q156NBEA | 2026-04-01 | 18.2 |
A007RE1Q156NBEA | 2026-07-01 | 18.5 |
A008RY2Q224SBEA | 2026-01-01 | 1.36 |
A008RY2Q224SBEA | 2026-04-01 | 1.25 |
A008RY2Q224SBEA | 2026-07-01 | 1.78 |
A011RE1Q156NBEA | 2026-01-01 | 3.7 |
A011RE1Q156NBEA | 2026-04-01 | 3.6 |
A011RE1Q156NBEA | 2026-07-01 | 3.6 |
A014RY2Q224SBEA | 2026-01-01 | 0.18 |
A014RY2Q224SBEA | 2026-04-01 | -0.53 |
A014RY2Q224SBEA | 2026-07-01 | 1.61 |
A019RE1Q156NBEA | 2026-01-01 | -2.6 |
A019RE1Q156NBEA | 2026-04-01 | -2.7 |
A019RE1Q156NBEA | 2026-07-01 | -3.7 |
A019RY2Q224SBEA | 2026-01-01 | -0.04 |
A019RY2Q224SBEA | 2026-04-01 | -1.1 |
A019RY2Q224SBEA | 2026-07-01 | -3.32 |
A023RC1Q027SBEA | 2026-01-01 | 32,037.666 |
A023RC1Q027SBEA | 2026-04-01 | 32,748.853 |
A191RL1Q225SBEA | 2026-01-01 | 2.5 |
A191RL1Q225SBEA | 2026-04-01 | 2.2 |
A191RL1Q225SBEA | 2026-07-01 | 3.4 |
A191RO1Q156NBEA | 2026-01-01 | 2.6 |
A191RO1Q156NBEA | 2026-04-01 | 2.2 |
A191RO1Q156NBEA | 2026-07-01 | 2.1 |
A191RP1Q027SBEA | 2026-01-01 | 5.8 |
A191RP1Q027SBEA | 2026-04-01 | 8.5 |
A191RP1Q027SBEA | 2026-07-01 | 5.6 |
A261RL1Q225SBEA | 2026-01-01 | 2.5 |
A261RL1Q225SBEA | 2026-04-01 | 2.6 |
A261RX1Q020SBEA | 2026-01-01 | 24,390.358 |
A261RX1Q020SBEA | 2026-04-01 | 24,550.051 |
A939RC0Q052SBEA | 2026-01-01 | 93,135 |
A939RC0Q052SBEA | 2026-04-01 | 95,005 |
A939RC0Q052SBEA | 2026-07-01 | 96,237 |
A939RX0Q048SBEA | 2026-01-01 | 70,857 |
A939RX0Q048SBEA | 2026-04-01 | 71,212 |
A939RX0Q048SBEA | 2026-07-01 | 71,751 |
B020RE1Q156NBEA | 2026-01-01 | 11.1 |
B020RE1Q156NBEA | 2026-04-01 | 11.7 |
B020RE1Q156NBEA | 2026-07-01 | 11.4 |
B021RE1Q156NBEA | 2026-01-01 | 13.7 |
B021RE1Q156NBEA | 2026-04-01 | 14.4 |
B021RE1Q156NBEA | 2026-07-01 | 15.1 |
B935RY2Q224SBEA | 2026-01-01 | 0.62 |
B935RY2Q224SBEA | 2026-04-01 | -0.09 |
B935RY2Q224SBEA | 2026-07-01 | 0.72 |
B985RY2Q224SBEA | 2026-01-01 | 0.6 |
B985RY2Q224SBEA | 2026-04-01 | 0.33 |
B985RY2Q224SBEA | 2026-07-01 | 0.23 |
BBKMCY | 2025-11-01 | -1.487075 |
BBKMCY | 2025-12-01 | -0.829618 |
BBKMCY | 2026-01-01 | -0.869208 |
BBKMCY | 2026-02-01 | 0.43303 |
BBKMCY | 2026-03-01 | -0.231091 |
BBKMCY | 2026-04-01 | 0.097552 |
BBKMCY | 2026-05-01 | -0.43759 |
BBKMCY | 2026-06-01 | -0.875531 |
BBKMCY | 2026-07-01 | 0.169927 |
BBKMCY | 2026-08-01 | -0.124998 |
BBKMGDP | 2025-11-01 | 0.473347 |
BBKMGDP | 2025-12-01 | 2.162236 |
BBKMGDP | 2026-01-01 | 2.655508 |
BBKMGDP | 2026-02-01 | 3.605977 |
BBKMGDP | 2026-03-01 | 2.174234 |
BBKMGDP | 2026-04-01 | 2.074598 |
BBKMGDP | 2026-05-01 | 1.799518 |
BBKMGDP | 2026-06-01 | 1.981866 |
CANQGSP | 2026-01-01 | 4,449,494.3 |
CANQGSP | 2026-04-01 | 4,530,521.4 |
CARQGSP | 2026-01-01 | 3,493,858.3 |
CARQGSP | 2026-04-01 | 3,515,203.1 |
CFNAI | 2025-11-01 | -0.29 |
CFNAI | 2025-12-01 | -0.06 |
CFNAI | 2026-01-01 | -0.05 |
CFNAI | 2026-02-01 | 0.23 |
CFNAI | 2026-03-01 | -0.11 |
CFNAI | 2026-04-01 | 0.11 |
CFNAI | 2026-05-01 | -0.07 |
CFNAI | 2026-06-01 | 0.01 |
CFNAI | 2026-07-01 | 0.12 |
CFNAI | 2026-08-01 | -0.08 |
CFNAI | 2026-09-01 | 0.07 |
CPGDPAI | 2026-01-01 | 2.5 |
CPGDPAI | 2026-04-01 | 2.2 |
FLNQGSP | 2026-01-01 | 1,875,301.6 |
FLNQGSP | 2026-04-01 | 1,909,471.3 |
FLRQGSP | 2026-01-01 | 1,400,893.9 |
FLRQGSP | 2026-04-01 | 1,411,321.1 |
GARQGSP | 2026-01-01 | 728,062.9 |
GARQGSP | 2026-04-01 | 733,586.6 |
GDI | 2026-01-01 | 32,058.712 |
GDI | 2026-04-01 | 32,752.528 |
US macro data: Growth (105 series)
105 growth series from US government sources, cleaned into one consistent format and ready for pandas. This free package holds the last 12 months (2025-11-01 on) of each series' latest values. It is refreshed weekly.
import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-growth/data/long.parquet") # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-growth/data/wide_monthly.parquet") # one column per series
or datasets.load_dataset("FinzData/macro-growth"). Wide tables by frequency: wide_monthly (3 series), wide_quarterly (98 series), wide_annual (2 series), wide_other (2 series). series.csv lists every series
with its title, units, frequency and how far back the full history goes.
The full history, point in time, through one API
The FinzData API serves every series back to its first observation, every revision (what was known on any past date, so backtests do not peek at later revisions), and daily updates, in the same format:
import finzdata as fz # pip install finzdata
c = fz.Client() # free API key: https://api.finzdata.com/
c.macro("GDP") # full history, latest values
c.macro("GDP", vintage="first") # as first released
c.macro("GDP", as_of="2020-03-01") # exactly what was known on that date
c.macro_series(category="growth") # the catalogue for this category
Most-watched series in this package
| ID | Title | Units | Frequency | Full history from |
|---|---|---|---|---|
GDP |
Gross Domestic Product | Billions of Dollars | Quarterly | 1946 |
GDPC1 |
Real Gross Domestic Product | Billions of Chained 2017 Dollars | Quarterly | 1947 |
A939RX0Q048SBEA |
Real gross domestic product per capita | Chained 2017 Dollars | Quarterly | 1947 |
A191RL1Q225SBEA |
Real Gross Domestic Product | Percent Change from Preceding Period | Quarterly | 1947 |
GDPPOT |
Real Potential Gross Domestic Product | Billions of Chained 2017 Dollars | Quarterly | 1949 |
NGDPSAXDCUSQ |
Nominal Gross Domestic Product (GDP) for United States | Millions of Domestic Currency | Quarterly | 1950 |
STLENI |
St. Louis Fed Economic News Index: Real GDP Nowcast | Percent Change at Annual Rate | Quarterly | 2013 |
CFNAI |
Chicago Fed National Activity Index | Index | Monthly | 1967 |
NGDPRSAXDCCAQ |
Real Gross Domestic Product (GDP) for Canada | Millions of Domestic Currency | Quarterly | 1961 |
GNP |
Gross National Product | Billions of Dollars | Quarterly | 1939 |
VAPGDPMA |
Value Added by Industry: Manufacturing as a Percentage of GDP | Percent | Quarterly | 2005 |
NGDPRSAXDCAUQ |
Real Gross Domestic Product (GDP) for Australia | Millions of Domestic Currency | Quarterly | 1959 |
A191RO1Q156NBEA |
Real Gross Domestic Product | Percent Change from Quarter One Year Ago | Quarterly | 1948 |
NGDPRSAXDCGBQ |
Real Gross Domestic Product (GDP) for United Kingdom | Millions of Domestic Currency | Quarterly | 1955 |
A191RP1Q027SBEA |
Gross Domestic Product | Percent Change from Preceding Period | Quarterly | 1947 |
A939RC0Q052SBEA |
Gross domestic product per capita | Dollars | Quarterly | 1947 |
GDPC1CTM |
FOMC Summary of Economic Projections for the Growth Rate of Real Gross Domestic Product, Central Tendency, Midpoint | Fourth Quarter to Fourth Quarter Percent Change | Annual | 2007 |
NGDPRNSAXDCINQ |
Real Gross Domestic Product (GDP) for India | Millions of Domestic Currency | Quarterly | 2004 |
GDI |
Gross Domestic Income | Billions of Dollars | Quarterly | 1947 |
NGDPRSAXDCMXQ |
Real Gross Domestic Product (GDP) for Mexico | Millions of Domestic Currency | Quarterly | 1993 |
NA000334Q |
Gross Domestic Product | Millions of Dollars | Quarterly | 1947 |
NGDPRSAXDCKRQ |
Real Gross Domestic Product for Republic of Korea | Millions of Domestic Currency | Quarterly | 1960 |
A006RE1Q156NBEA |
Shares of gross domestic product: Gross private domestic investment | Percent | Quarterly | 1947 |
BBKMGDP |
Brave-Butters-Kelley Real Gross Domestic Product | Annualized Percent Change from Preceding Period | Monthly | 1960 |
NGDPPOT |
Nominal Potential Gross Domestic Product | Billions of Dollars | Quarterly | 1949 |
All 105 series: series.csv. Browse them at https://finzdata.com/macro/category/growth.
More from FinzData
- Every macro category: https://huggingface.co/FinzData · index on GitHub: https://github.com/FinzData/macro-data
- Survivorship-free US stock prices since 2000, COT positioning, 13F holdings, insider trades, short interest
- Pricing (history bundles from $195 once): https://finzdata.com/pricing
- Python client (open source): https://github.com/FinzData/finzdata-python
Source: U.S. government agencies via FRED/ALFRED (Federal Reserve Bank of St. Louis). Compiled by FinzData. Data for research, not investment advice.
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