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series_id
large_stringclasses
13 values
date
date32
value
float64
16.9k
343k
B230RC0Q173SBEA
2026-01-01
342,583
B230RC0Q173SBEA
2026-04-01
342,749
CNP16OV
2025-11-01
274,633
CNP16OV
2025-12-01
274,816
CNP16OV
2026-01-01
274,676
CNP16OV
2026-02-01
274,766
CNP16OV
2026-03-01
274,858
CNP16OV
2026-04-01
274,955
CNP16OV
2026-05-01
275,054
CNP16OV
2026-06-01
275,166
CNP16OV
2026-07-01
275,282
CNP16OV
2026-08-01
275,415
CNP16OV
2026-09-01
275,554
LNU00000006
2025-11-01
35,961
LNU00000006
2025-12-01
35,995
LNU00000006
2026-01-01
35,364
LNU00000006
2026-02-01
35,388
LNU00000006
2026-03-01
35,411
LNU00000006
2026-04-01
35,434
LNU00000006
2026-05-01
35,455
LNU00000006
2026-06-01
35,477
LNU00000006
2026-07-01
35,500
LNU00000006
2026-08-01
35,526
LNU00000006
2026-09-01
35,553
LNU00000012
2025-11-01
17,808
LNU00000012
2025-12-01
17,801
LNU00000012
2026-01-01
17,617
LNU00000012
2026-02-01
17,613
LNU00000012
2026-03-01
17,608
LNU00000012
2026-04-01
17,603
LNU00000012
2026-05-01
17,579
LNU00000012
2026-06-01
17,555
LNU00000012
2026-07-01
17,531
LNU00000012
2026-08-01
17,508
LNU00000012
2026-09-01
17,486
LNU00000036
2025-11-01
21,965
LNU00000036
2025-12-01
21,968
LNU00000036
2026-01-01
22,236
LNU00000036
2026-02-01
22,203
LNU00000036
2026-03-01
22,170
LNU00000036
2026-04-01
22,138
LNU00000036
2026-05-01
22,130
LNU00000036
2026-06-01
22,123
LNU00000036
2026-07-01
22,116
LNU00000036
2026-08-01
22,114
LNU00000036
2026-09-01
22,112
LNU00000060
2025-11-01
131,364
LNU00000060
2025-12-01
131,469
LNU00000060
2026-01-01
129,994
LNU00000060
2026-02-01
130,071
LNU00000060
2026-03-01
130,096
LNU00000060
2026-04-01
130,073
LNU00000060
2026-05-01
130,111
LNU00000060
2026-06-01
130,139
LNU00000060
2026-07-01
130,173
LNU00000060
2026-08-01
130,195
LNU00000060
2026-09-01
130,274
LNU00024230
2025-11-01
103,496
LNU00024230
2025-12-01
103,578
LNU00024230
2026-01-01
104,829
LNU00024230
2026-02-01
104,878
LNU00024230
2026-03-01
104,984
LNU00024230
2026-04-01
105,140
LNU00024230
2026-05-01
105,235
LNU00024230
2026-06-01
105,349
LNU00024230
2026-07-01
105,462
LNU00024230
2026-08-01
105,598
LNU00024230
2026-09-01
105,682
LNU00049526
2025-11-01
17,131
LNU00049526
2025-12-01
17,092
LNU00049526
2026-01-01
17,071
LNU00049526
2026-02-01
17,045
LNU00049526
2026-03-01
17,015
LNU00049526
2026-04-01
16,989
LNU00049526
2026-05-01
16,961
LNU00049526
2026-06-01
16,939
LNU00049526
2026-07-01
16,912
LNU00049526
2026-08-01
16,884
LNU00049526
2026-09-01
16,860
LNU00073395
2025-11-01
48,558
LNU00073395
2025-12-01
48,905
LNU00073395
2026-01-01
49,750
LNU00073395
2026-02-01
49,316
LNU00073395
2026-03-01
49,725
LNU00073395
2026-04-01
49,660
LNU00073395
2026-05-01
49,061
LNU00073395
2026-06-01
48,564
LNU00073395
2026-07-01
48,109
LNU00073395
2026-08-01
48,504
LNU00073395
2026-09-01
48,875
LNU00073413
2025-11-01
226,075
LNU00073413
2025-12-01
225,911
LNU00073413
2026-01-01
224,925
LNU00073413
2026-02-01
225,450
LNU00073413
2026-03-01
225,133
LNU00073413
2026-04-01
225,295
LNU00073413
2026-05-01
225,993
LNU00073413
2026-06-01
226,602
LNU00073413
2026-07-01
227,173
LNU00073413
2026-08-01
226,911
End of preview. Expand in Data Studio

US macro data: Population (13 series)

13 population series from US government sources, cleaned into one consistent format and ready for pandas. This free package holds the last 12 months (2025-10-26 on) of each series' latest values. It is refreshed weekly.

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

or datasets.load_dataset("FinzData/macro-population"). Wide tables by frequency: wide_monthly (12 series), wide_quarterly (1 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("POPTHM")                        # full history, latest values
c.macro("POPTHM", vintage="first")       # as first released
c.macro("POPTHM", as_of="2020-03-01")    # exactly what was known on that date
c.macro_series(category="population")          # the catalogue for this category

Most-watched series in this package

ID Title Units Frequency Full history from
POPTHM Population Thousands Monthly 1959
CNP16OV Population Level Thousands of Persons Monthly 1948
LNU00074597 Population - With a Disability, 16 Years and over Thousands of Persons Monthly 2008
POP Total Population: All Ages including Armed Forces Overseas Thousands Monthly 1952
LNU00073395 Population Level - Foreign Born Thousands of Persons Monthly 2007
LNU00024230 Population Level - 55 Yrs. & over Thousands of Persons Monthly 1948
LNU00000060 Population Level - 25-54 Yrs. Thousands of Persons Monthly 1948
B230RC0Q173SBEA Population Thousands Quarterly 1947
LNU00000012 Population Level - 16-19 Yrs. Thousands of Persons Monthly 1948
LNU00073413 Population Level - Native Born Thousands of Persons Monthly 2007
LNU00000036 Population Level - 20-24 Yrs. Thousands of Persons Monthly 1948
LNU00000006 Population Level - Black or African American Thousands of Persons Monthly 1972
LNU00049526 Population Level - Total Veterans, 18 Years and over Thousands of Persons Monthly 2000

All 13 series: series.csv. Browse them at https://finzdata.com/macro/category/population.

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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