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 |
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
- 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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