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value
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-14,199
26.5M
A024RA3Q086SBEA
2026-01-01
134.198
A024RA3Q086SBEA
2026-04-01
135.872
A024RA3Q086SBEA
2026-07-01
137.632
A024RC1Q027SBEA
2026-01-01
4,388.547
A024RC1Q027SBEA
2026-04-01
4,470.551
A024RC1Q027SBEA
2026-07-01
4,575.914
A024RG3Q086SBEA
2026-01-01
125.836
A024RG3Q086SBEA
2026-04-01
126.608
A024RG3Q086SBEA
2026-07-01
127.935
A024RL1Q225SBEA
2026-01-01
4.4
A024RL1Q225SBEA
2026-04-01
5.1
A024RL1Q225SBEA
2026-07-01
5.3
A024RX1Q020SBEA
2026-01-01
3,487.891
A024RX1Q020SBEA
2026-04-01
3,531.391
A024RX1Q020SBEA
2026-07-01
3,577.15
A136RA3Q086SBEA
2026-01-01
51.787
A136RA3Q086SBEA
2026-04-01
54.512
A136RA3Q086SBEA
2026-07-01
52.505
A136RC1Q027SBEA
2026-01-01
44.478
A136RC1Q027SBEA
2026-04-01
46.804
A136RC1Q027SBEA
2026-07-01
45.165
A136RG3Q086SBEA
2026-01-01
122.165
A136RG3Q086SBEA
2026-04-01
122.128
A136RG3Q086SBEA
2026-07-01
122.356
A136RL1Q225SBEA
2026-01-01
-6.4
A136RL1Q225SBEA
2026-04-01
22.8
A136RL1Q225SBEA
2026-07-01
-13.9
A136RX1Q020SBEA
2026-01-01
36.407
A136RX1Q020SBEA
2026-04-01
38.323
A136RX1Q020SBEA
2026-07-01
36.912
A137RC1Q027SBEA
2026-01-01
49.115
A137RC1Q027SBEA
2026-04-01
47.795
A137RC1Q027SBEA
2026-07-01
46.963
A137RG3Q086SBEA
2026-01-01
130.993
A137RG3Q086SBEA
2026-04-01
130.59
A137RG3Q086SBEA
2026-07-01
131.496
A137RX1Q020SBEA
2026-01-01
37.494
A137RX1Q020SBEA
2026-04-01
36.599
A137RX1Q020SBEA
2026-07-01
35.714
A1615C1Q027SBEA
2026-01-01
485.515
A1615C1Q027SBEA
2026-04-01
494.075
A1615C1Q027SBEA
2026-07-01
504.814
A1618C1Q027SBEA
2026-01-01
94.315
A1618C1Q027SBEA
2026-04-01
95.221
A1618C1Q027SBEA
2026-07-01
96.176
A194RA3Q086SBEA
2026-01-01
103.757
A194RA3Q086SBEA
2026-04-01
103.721
A194RA3Q086SBEA
2026-07-01
103.75
A194RC1Q027SBEA
2026-01-01
2,475.866
A194RC1Q027SBEA
2026-04-01
2,494.006
A194RC1Q027SBEA
2026-07-01
2,514.733
A194RG3Q086SBEA
2026-01-01
134.831
A194RG3Q086SBEA
2026-04-01
135.866
A194RG3Q086SBEA
2026-07-01
136.957
A194RX1Q020SBEA
2026-01-01
1,835.711
A194RX1Q020SBEA
2026-04-01
1,835.072
A194RX1Q020SBEA
2026-07-01
1,835.587
A262RA3Q086SBEA
2026-01-01
132.118
A262RA3Q086SBEA
2026-04-01
133.629
A262RA3Q086SBEA
2026-07-01
135.211
A262RG3Q086SBEA
2026-01-01
126.433
A262RG3Q086SBEA
2026-04-01
127.362
A262RG3Q086SBEA
2026-07-01
128.745
A262RL1Q225SBEA
2026-01-01
4.1
A262RL1Q225SBEA
2026-04-01
4.7
A262RL1Q225SBEA
2026-07-01
4.8
A262RX1Q020SBEA
2026-01-01
4,160.32
A262RX1Q020SBEA
2026-04-01
4,207.925
A262RX1Q020SBEA
2026-07-01
4,257.717
A264RA3Q086SBEA
2026-01-01
122.439
A264RA3Q086SBEA
2026-04-01
123.207
A264RA3Q086SBEA
2026-07-01
123.968
A264RC1Q027SBEA
2026-01-01
870.935
A264RC1Q027SBEA
2026-04-01
888.209
A264RC1Q027SBEA
2026-07-01
905.13
A264RG3Q086SBEA
2026-01-01
129.365
A264RG3Q086SBEA
2026-04-01
131.108
A264RG3Q086SBEA
2026-07-01
132.785
A264RL1Q225SBEA
2026-01-01
2.6
A264RL1Q225SBEA
2026-04-01
2.5
A264RL1Q225SBEA
2026-07-01
2.5
A264RX1Q020SBEA
2026-01-01
673.277
A264RX1Q020SBEA
2026-04-01
677.502
A264RX1Q020SBEA
2026-07-01
681.687
A265RA3Q086SBEA
2026-01-01
123.212
A265RA3Q086SBEA
2026-04-01
124.037
A265RA3Q086SBEA
2026-07-01
124.854
A265RC1Q027SBEA
2026-01-01
751.376
A265RC1Q027SBEA
2026-04-01
766.047
A265RC1Q027SBEA
2026-07-01
780.844
A265RG3Q086SBEA
2026-01-01
127.644
A265RG3Q086SBEA
2026-04-01
129.271
A265RG3Q086SBEA
2026-07-01
130.905
A265RL1Q225SBEA
2026-01-01
2.8
A265RL1Q225SBEA
2026-04-01
2.7
A265RL1Q225SBEA
2026-07-01
2.7
A265RX1Q020SBEA
2026-01-01
588.685
A265RX1Q020SBEA
2026-04-01
592.626
A265RX1Q020SBEA
2026-07-01
596.531
A266RA3Q086SBEA
2026-01-01
117.804
End of preview. Expand in Data Studio

US macro data: Consumption (1,602 series)

1,602 consumption series from US government sources, cleaned into one consistent format and ready for pandas. This free package holds the last 12 months (2025-10-30 on) of each series' latest values. It is refreshed weekly.

import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-consumption/data/long.parquet")      # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-consumption/data/wide_monthly.parquet")  # one column per series

or datasets.load_dataset("FinzData/macro-consumption"). Wide tables by frequency: wide_monthly (119 series), wide_quarterly (1462 series), wide_annual (14 series), wide_other (7 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("PCEPILFE")                        # full history, latest values
c.macro("PCEPILFE", vintage="first")       # as first released
c.macro("PCEPILFE", as_of="2020-03-01")    # exactly what was known on that date
c.macro_series(category="consumption")          # the catalogue for this category

Most-watched series in this package

ID Title Units Frequency Full history from
PCEPILFE Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index) Index 2017=100 Monthly 1959
DPCCRV1Q225SBEA Personal Consumption Expenditures (PCE) Excluding Food and Energy (Chain-Type Price Index) Percent Change from Preceding Period Quarterly 1959
PCETRIM12M159SFRBDAL Trimmed Mean PCE Inflation Rate Percent Change from Year Ago Monthly 1978
PCECC96 Real Personal Consumption Expenditures Billions of Chained 2017 Dollars Quarterly 1947
DPCERE1Q156NBEA Shares of gross domestic product: Personal consumption expenditures Percent Quarterly 1947
PCEC Personal Consumption Expenditures Billions of Dollars Quarterly 1939
PCETRIM1M158SFRBDAL Trimmed Mean PCE Inflation Rate Percent Change at Annual Rate Monthly 1977
PCEDG Personal Consumption Expenditures: Durable Goods Billions of Dollars Monthly 1959
FDEFX Federal Government: National Defense Consumption Expenditures and Gross Investment Billions of Dollars Quarterly 1946
IA001260M Personal Consumption Expenditures: Services Excluding Energy and Housing (Chain-Type Price Index) Index 2017=100 Monthly 1959
PCECTPI Personal Consumption Expenditures: Chain-type Price Index Index 2017=100 Quarterly 1947
GCEC1 Real Government Consumption Expenditures and Gross Investment Billions of Chained 2017 Dollars Quarterly 1947
PCES Personal Consumption Expenditures: Services Billions of Dollars Monthly 1959
PCEDGC96 Real Personal Consumption Expenditures: Durable Goods Billions of Chained 2017 Dollars Monthly 1959
A794RX0Q048SBEA Real personal consumption expenditures per capita Chained 2017 Dollars Quarterly 1947
DPCERD3Q086SBEA Personal consumption expenditures (implicit price deflator) Index 2017=100 Quarterly 1947
GCE Government Consumption Expenditures and Gross Investment Billions of Dollars Quarterly 1939
PCEND Personal Consumption Expenditures: Nondurable Goods Billions of Dollars Monthly 1959
BPCCRO1Q156NBEA Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index) Percent Change from Quarter One Year Ago Quarterly 1960
DFXARC1M027SBEA Personal consumption expenditures: Food Billions of Dollars Monthly 1959
DGDSRX1 Real Personal Consumption Expenditures: Goods Billions of Chained 2017 Dollars Monthly 1995
DPCERY2Q224SBEA Contributions to percent change in real gross domestic product: Personal consumption expenditures Percentage Points at Annual Rate Quarterly 1947
PCESC96 Real Personal Consumption Expenditures: Services Billions of Chained 2017 Dollars Monthly 1959
DHLCRC1Q027SBEA Personal consumption expenditures: Services: Health care Billions of Dollars Quarterly 1959
PCDG Personal Consumption Expenditures: Durable Goods Billions of Dollars Quarterly 1939

All 1,602 series: series.csv. Browse them at https://finzdata.com/macro/category/consumption.

More from FinzData

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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