diff --git a/CATALOG.md b/CATALOG.md index 9980b97..742be84 100644 --- a/CATALOG.md +++ b/CATALOG.md @@ -6,14 +6,17 @@ The migrated-dataset registry, **auto-generated** from the sidecar manifests (`lectures/*.yml`). Do not edit by hand — run `python scripts/build_catalog.py`. A dataset appears here once it has a manifest; files not yet migrated are tracked in [PLAN.md](PLAN.md) Phase 9. -**4 datasets migrated** · 1.8 MB total · 3 permitted / 1 restricted redistribution +**7 datasets migrated** · 2.0 MB total · 6 permitted / 1 restricted redistribution | Dataset | Class | Source | Licence | Redist. | Integrity | Builder | Size | Used by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| [**ames_house_prices.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/ames_house_prices.csv)
Ames, Iowa — residential house sales, 2006-2010 | constructed | [Ames Housing data (De Cock 2011), Journal of Statistics Education](http://jse.amstat.org/v19n3/decock.pdf) | | ✅ permitted | ✅ verified | ✅ committed | 75.2 KB | [lecture-python-intro · observed_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/observed_distributions.md) | | [**countries.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/countries.csv)
WorldData.info country reference table | verbatim | [WorldData.info — country data downloads](https://www.worlddata.info/downloads/) | Proprietary — © WorldData.info, all rights reserved | ⚠️ restricted | ⚠️ unverifiable | n/a (verbatim) | 48.4 KB | [lecture-python-programming · pandas_panel.md](https://github.com/QuantEcon/lecture-python-programming/blob/main/lectures/pandas_panel.md)
[lecture-python.myst · pandas_panel.md](https://github.com/QuantEcon/lecture-python.myst/blob/main/lectures/pandas_panel.md) | | [**employ.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/employ.csv)
Eurostat employment in Europe — by age and sex, 2007–2016 | constructed | [Eurostat — Employment database](https://ec.europa.eu/eurostat/data/database) | Eurostat reuse (Commission Decision 2011/833/EU) | ✅ permitted | ⚠️ unverifiable | ⚠️ unrecovered | 1.6 MB | [lecture-python-programming · pandas_panel.md](https://github.com/QuantEcon/lecture-python-programming/blob/main/lectures/pandas_panel.md)
[lecture-python.myst · pandas_panel.md](https://github.com/QuantEcon/lecture-python.myst/blob/main/lectures/pandas_panel.md) | +| [**japan_deaths_by_age.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/japan_deaths_by_age.csv)
Japan — deaths by single year of age, 2023 | constructed | [United Nations, Department of Economic and Social Affairs, Population Division — World Population Prospects 2024](https://population.un.org/wpp/downloads) | CC BY 3.0 IGO | ✅ permitted | ✅ verified | ✅ committed | 1.7 KB | [lecture-python-intro · observed_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/observed_distributions.md) | | [**lingcod_msy_recovery.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/lingcod_msy_recovery.csv)
Pacific Coast lingcod — biomass and fishing pressure relative to MSY | constructed | [RAM Legacy Stock Assessment Database](https://www.ramlegacy.org/) | CC BY 4.0 | ✅ permitted | ⚠️ unverifiable | ⚠️ unrecovered | 2.3 KB | [lecture-python-intro · msy_fishery.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/msy_fishery.md) | | [**realwage.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/realwage.csv)
OECD real minimum wages — 32 countries, 2006–2016 | constructed | [OECD — Real minimum wages (RMW)](https://stats.oecd.org/Index.aspx?DataSetCode=RMW) | CC BY 4.0 | ✅ permitted | ⚠️ unverifiable | ⚠️ unrecovered | 118.7 KB | [lecture-python-programming · pandas_panel.md](https://github.com/QuantEcon/lecture-python-programming/blob/main/lectures/pandas_panel.md)
[lecture-python.myst · pandas_panel.md](https://github.com/QuantEcon/lecture-python.myst/blob/main/lectures/pandas_panel.md) | +| [**us_adult_heights.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/us_adult_heights.csv)
United States — adult standing height by sex, NHANES 2015-2018 | constructed | [National Health and Nutrition Examination Survey (NHANES), US Centers for Disease Control and Prevention, National Center for Health Statistics](https://www.cdc.gov/nchs/nhanes/index.htm) | US Government work — public domain | ✅ permitted | ✅ verified | ✅ committed | 123.1 KB | [lecture-python-intro · prob_dist.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/prob_dist.md)
[lecture-python-intro · observed_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/observed_distributions.md) | --- diff --git a/lectures/ames_house_prices.csv b/lectures/ames_house_prices.csv new file mode 100644 index 0000000..0da6e2d --- /dev/null +++ b/lectures/ames_house_prices.csv @@ -0,0 +1,2931 @@ +price,bedrooms,living_area_sqft,year_built,neighborhood +215000,3,1656,1960,NAmes +105000,2,896,1961,NAmes +172000,3,1329,1958,NAmes +244000,3,2110,1968,NAmes +189900,3,1629,1997,Gilbert +195500,3,1604,1998,Gilbert +213500,2,1338,2001,StoneBr +191500,2,1280,1992,StoneBr +236500,2,1616,1995,StoneBr +189000,3,1804,1999,Gilbert +175900,3,1655,1993,Gilbert +185000,3,1187,1992,Gilbert +180400,3,1465,1998,Gilbert +171500,2,1341,1990,Gilbert +212000,1,1502,1985,StoneBr +538000,4,3279,2003,StoneBr +164000,4,1752,1988,Gilbert +394432,1,1856,2010,StoneBr +141000,2,864,1951,Gilbert +210000,3,2073,1978,NWAmes +190000,3,1844,1977,NWAmes 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+350000,2,2113,1995,NoRidge +341000,3,2418,1993,NoRidge +287500,4,2197,1993,NoRidge +271000,3,2127,1994,NoRidge +301500,2,1987,1994,NoRidge +235128,3,1580,2007,Somerst +214000,4,1933,2007,Somerst +232000,3,1574,2006,Somerst +240000,3,1578,2006,Somerst +264132,2,1506,2008,Somerst +245000,3,1563,2007,Somerst +227680,3,1428,2008,Somerst +257000,3,1861,2007,Somerst +297900,3,1874,2007,Somerst +247000,3,1460,2006,Somerst +212700,3,1372,2007,Somerst +250580,3,1372,2007,Somerst +182000,3,1428,2007,Somerst +226700,3,1768,2005,Somerst +250000,3,1992,2003,Somerst +250000,3,1958,2005,Somerst +339750,3,2078,2007,Somerst +205950,3,1665,2007,Somerst +256000,3,1660,2007,Somerst +230348,2,1218,2007,Somerst +207500,3,1728,2003,Somerst +141000,3,1080,2004,SawyerW +188000,3,1696,2003,SawyerW +192500,3,1663,2003,SawyerW +146000,3,1175,2005,SawyerW +159000,3,1114,2004,SawyerW +155000,3,1114,2003,SawyerW +173000,3,1640,2004,SawyerW +170000,3,1162,2007,SawyerW +182000,3,1609,1995,SawyerW +163000,3,1680,1993,SawyerW +193500,3,1679,1994,SawyerW +189000,3,1657,1994,SawyerW +190500,3,1677,2001,SawyerW +175000,3,1737,1992,SawyerW +179200,3,1294,1991,SawyerW +187000,3,1710,1993,SawyerW +193000,2,1617,1993,SawyerW +153900,2,1199,1993,SawyerW +128000,3,984,1963,Sawyer +144000,2,912,1962,Sawyer +135000,3,864,1961,Sawyer +115000,3,890,1968,Sawyer +119916,3,864,1963,Sawyer +142000,3,912,1961,Sawyer +192000,3,1430,1974,Veenker +140000,4,1641,1972,Sawyer +290000,4,2683,1990,NoRidge +372500,4,2786,1993,NoRidge +196000,2,1391,2004,Somerst +171900,2,1250,2006,Somerst +178000,1,1245,2004,Somerst +165000,2,1200,2005,Somerst +146000,2,1392,2007,Somerst +146000,2,1392,2007,Somerst +179400,3,1501,1999,Somerst +172900,3,1549,1999,Somerst +300000,2,1638,2000,Somerst +285000,1,1310,2001,Somerst +290000,2,1419,2001,Somerst +305000,2,1557,1999,Somerst +328900,2,1709,1999,Somerst +160000,2,1252,1999,Somerst +170000,3,1404,1999,Somerst +223000,3,1789,2001,Somerst +170000,3,1586,1999,Somerst +200000,3,1607,1999,Somerst +345000,2,2393,1998,Somerst +245500,1,1288,1996,Veenker +217500,1,1239,1995,Veenker +324000,1,1719,1993,Veenker +162500,3,1232,1984,Veenker +150000,3,2944,1977,Veenker +290000,3,1671,1976,Veenker +205000,3,1812,2002,Somerst +174000,3,1682,1970,NWAmes +183000,4,1427,1969,NWAmes +150000,4,1740,1968,NWAmes +145000,3,1258,1971,NWAmes +215000,4,1620,1967,NWAmes +140500,4,1625,1965,NWAmes +240000,4,2614,1974,NWAmes +141000,4,1464,1968,NAmes +147000,2,912,1965,NAmes +135000,3,925,1965,NAmes +145000,6,1728,1965,NAmes +142600,6,1728,1965,NAmes +135000,4,1656,1973,NWAmes +170000,4,1670,1971,NWAmes +299800,4,2715,1976,NWAmes +185000,4,1876,1974,NWAmes +173000,4,1632,1966,NWAmes +178400,3,999,1961,NAmes +176000,4,1978,1968,NWAmes +98000,2,1014,1956,NAmes +109008,3,907,1957,NAmes +135750,2,952,1956,NAmes +155000,3,1128,1963,NAmes +180500,3,1425,1964,NAmes +174900,3,1165,1966,NAmes +140000,3,1136,1965,NAmes +145000,3,1092,1964,NAmes +179900,3,1265,1965,NAmes 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+100000,4,1764,1957,NAmes +118500,2,1216,1947,NAmes +146000,5,1824,1957,NAmes +89900,1,869,1948,OldTown +114000,3,1159,1925,OldTown +140000,3,1558,1945,NAmes +86900,2,672,1940,OldTown +139000,3,1436,1922,OldTown +106250,3,1294,1900,OldTown +171000,4,1716,1962,NAmes +139000,3,1044,1968,NAmes +186000,3,1312,1958,NAmes +157000,2,1253,1960,NAmes +148000,3,1081,1960,NAmes +242000,4,2069,1960,NAmes +116000,3,910,1953,NAmes +94000,2,876,1951,NAmes +98300,3,1048,1950,NAmes +127000,4,1507,1960,NAmes +159000,3,1256,1959,NAmes +125900,3,1027,1962,NAmes +155000,3,1269,1960,NAmes +138000,3,1320,1948,NAmes +112500,2,984,1954,NAmes +105500,2,832,1954,NAmes +127500,4,1278,1954,NAmes +130000,6,1800,1961,NAmes +150000,3,1588,1955,NAmes +109500,2,825,1954,NAmes +140000,4,1796,1963,NAmes +167900,4,1611,1964,NAmes +136870,3,1117,1963,NAmes +143000,3,1133,2008,OldTown +73000,2,764,1925,OldTown +157500,3,1539,1904,OldTown +122600,3,1323,1948,OldTown +111000,2,1360,1910,OldTown +256000,5,2792,1920,OldTown 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+97000,2,882,1956,OldTown +115500,2,1093,1958,OldTown +138000,2,1143,1928,BrkSide +108000,3,1668,1945,BrkSide +79500,2,935,1930,BrkSide +153000,3,1738,1935,BrkSide +109500,2,784,1924,BrkSide +124000,3,1210,1941,BrkSide +106900,3,1290,1900,BrkSide +164900,3,1672,1940,BrkSide +119000,2,949,1924,BrkSide +120000,3,1158,1929,BrkSide +157000,3,1497,1937,BrkSide +105000,2,1342,1939,BrkSide +113000,3,1013,1926,BrkSide +115000,3,1216,1920,BrkSide +79000,2,896,1946,BrkSide +123000,2,1136,1929,BrkSide +81300,1,808,1945,BrkSide +162900,3,1573,1930,BrkSide +150000,4,2009,1939,BrkSide +123500,4,1902,1923,OldTown +129900,2,1774,1931,OldTown +128000,3,1362,1920,BrkSide +68500,1,520,1927,BrkSide +127000,2,1285,1948,BrkSide +207000,4,1848,1923,BrkSide +184000,4,1820,1925,BrkSide +162500,4,1716,1915,BrkSide +145000,3,1505,1940,BrkSide +130000,2,838,1918,BrkSide +160000,5,1984,1910,OldTown +161000,3,1666,1925,OldTown +127500,3,1609,1910,OldTown +93850,1,768,1930,IDOTRR +136000,4,1536,1952,IDOTRR +141000,4,1969,1938,IDOTRR +120000,2,1308,1915,IDOTRR +127500,2,1040,1925,IDOTRR +127000,3,1236,1925,IDOTRR +89500,3,1406,1920,IDOTRR +125000,2,825,1955,OldTown +79900,4,1198,1920,OldTown +85000,1,759,1912,IDOTRR +82375,3,1344,1915,IDOTRR +135000,3,1469,1906,IDOTRR +124000,3,1054,1947,Sawyer +134000,3,1211,1968,Sawyer +135000,4,1271,1948,Sawyer +134432,4,1588,1959,ClearCr +127500,4,1355,1940,Edwards +148000,3,1075,1963,Sawyer +129500,3,1040,1965,Sawyer +157500,3,1096,1978,Sawyer +109900,3,904,1966,Sawyer +145000,3,992,1967,Sawyer +130000,3,894,1967,Sawyer +153000,3,1034,1978,Sawyer +120000,3,1073,1967,Sawyer +152000,2,1126,1984,Sawyer +108959,0,1258,1969,Sawyer +95541,3,1140,1920,Sawyer +80000,3,1006,1959,Sawyer +149350,3,1228,1966,Sawyer +144900,3,960,1963,Sawyer +185000,1,1188,1956,Sawyer +165000,3,1383,1954,Sawyer +235000,1,1252,1960,ClearCr +140000,3,1601,1958,Sawyer +108000,2,672,1940,Sawyer +145000,3,1721,1960,ClearCr +152400,2,1350,1941,Sawyer +144000,3,904,1970,Sawyer +173000,2,1291,1996,ClearCr +192000,4,2022,1977,ClearCr +245000,3,1524,1979,ClearCr +241500,2,1363,1954,ClearCr +256000,2,1663,1954,ClearCr +177000,2,1357,1959,Edwards +125600,2,1079,1948,Edwards +155000,3,1094,1953,Edwards +240000,5,2358,1908,ClearCr +260000,2,1518,1940,Edwards +185000,4,1674,1918,Edwards +235000,4,2108,1935,Edwards +159900,3,1509,1956,Edwards +99600,2,864,1975,Edwards +134500,3,1269,1962,Edwards +119000,4,1367,1900,Edwards +107500,3,1347,1910,Edwards +125000,3,1164,1950,Edwards +79000,2,1096,1924,Edwards +95000,3,1111,1941,Edwards +262280,4,2329,1990,SawyerW +260000,4,2814,1995,SawyerW +226500,3,1626,1994,SawyerW +225000,4,2200,1993,SawyerW +229000,3,2037,1996,SawyerW +177439,3,1356,2007,CollgCr +248500,3,1615,2007,CollgCr +475000,3,2276,2008,CollgCr +395039,3,1766,2008,CollgCr +228000,3,2097,1993,SawyerW +225000,3,1511,1995,SawyerW +195000,2,1643,1966,ClearCr +200000,4,1688,1997,ClearCr +155000,3,990,1994,CollgCr +144000,3,1099,1995,CollgCr 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+158450,2,1452,1964,Sawyer +115400,3,1005,1964,Sawyer +118500,3,1029,1961,Sawyer +165000,3,1020,1964,Sawyer +123000,3,1040,1966,Sawyer +129000,3,913,1966,Sawyer +125000,3,894,1966,Sawyer +108000,3,948,1968,Sawyer +119900,2,868,1971,Sawyer +115000,3,897,1968,Sawyer +134500,2,943,1967,Sawyer +127000,3,955,1966,Sawyer +129000,3,894,1968,Sawyer +143500,3,1285,1977,Sawyer +129000,3,912,1966,Sawyer +187000,3,1375,1956,Sawyer +158000,4,2654,1920,Sawyer +112000,3,1416,1918,Sawyer +165250,2,1302,1940,Sawyer +150000,3,1299,1954,Sawyer +137000,3,1176,1958,Sawyer +89500,3,998,1945,Sawyer +302000,2,1824,1971,ClearCr +182000,3,1522,1984,ClearCr +211000,4,2060,1988,ClearCr +216000,3,1325,1948,Edwards +130000,3,1630,1953,Edwards +109900,2,1242,1946,Edwards +255000,4,2422,1954,ClearCr +243000,4,2158,1950,Edwards +154000,3,1626,1954,Edwards +118000,3,864,1958,SWISU +80000,3,943,1958,Edwards +150000,4,1440,1977,Edwards +86000,2,605,1920,Edwards +130000,2,1038,1984,Edwards +125000,4,1342,1951,Edwards 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+177500,3,1200,1995,CollgCr +175000,3,1308,1997,CollgCr +211000,3,1796,1998,CollgCr +146000,3,990,1994,CollgCr +147900,3,1097,1996,CollgCr +123600,3,990,1994,CollgCr +204750,3,1786,1997,CollgCr +235500,3,1873,1999,CollgCr +217000,3,1753,1998,CollgCr +190550,3,1690,2001,CollgCr +217500,3,1842,2000,CollgCr +226000,3,1479,2003,CollgCr +180000,3,1668,2003,CollgCr +131000,3,894,1974,CollgCr +153500,3,1025,1976,CollgCr +160000,3,1009,1977,CollgCr +134900,3,1040,1977,CollgCr +130000,3,907,1977,CollgCr +120000,3,879,1975,CollgCr +112500,3,882,1972,CollgCr +122000,3,969,1975,CollgCr +127000,3,864,1972,CollgCr +117000,3,875,1972,CollgCr +305900,3,2000,2004,CollgCr +228500,3,2169,2002,CollgCr +270000,3,1673,2000,CollgCr +192000,3,1459,2002,CollgCr +213000,3,1932,2003,CollgCr +200000,3,1729,2002,CollgCr +267000,4,2452,1998,CollgCr +228000,3,1592,1994,CollgCr +275000,4,2439,2001,CollgCr +211000,3,1992,1996,CollgCr +285000,3,1668,2002,CollgCr +240000,3,2090,1995,CollgCr +131500,1,848,2003,CollgCr 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+200624,4,2515,1957,Crawfor +315000,2,2726,1986,Crawfor +115000,3,1517,1919,IDOTRR +141000,3,1215,1940,IDOTRR +87000,2,803,1941,IDOTRR +129850,3,1601,1925,IDOTRR +100000,3,1123,1939,IDOTRR +150909,3,1828,1923,IDOTRR +75200,2,816,1922,IDOTRR +118400,2,1012,1920,IDOTRR +67000,2,1077,1920,IDOTRR +152000,4,1558,1950,IDOTRR +68104,1,845,1939,IDOTRR +119600,4,1991,1895,IDOTRR +140000,3,1073,1957,IDOTRR +124900,2,1001,1930,IDOTRR +375000,2,1625,2006,Crawfor +235000,2,1625,2006,Crawfor +183500,2,1299,1979,Mitchel +156000,2,912,1978,Mitchel +134000,3,1080,1983,Mitchel +215000,3,1392,2002,Mitchel +245000,1,1409,2002,Mitchel +210000,3,1478,1991,Mitchel +148000,3,918,1975,Mitchel +143000,2,1026,1974,Mitchel +244000,2,1501,1987,Timber +330000,1,1502,1998,GrnHill +170000,2,1328,1959,Timber +257000,4,2279,1958,Timber +231713,3,1689,2006,Timber +267300,2,1564,2006,Timber +265900,2,1484,2006,Timber +239000,3,1571,2007,Timber +248900,3,1646,2006,Timber +174000,3,1240,1985,Timber 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Used as an example of a strongly right-skewed, + roughly lognormal observed distribution. + +# Constructed, not verbatim: the upstream workbook carries 82 columns assembled +# for an end-of-semester regression project. Selecting five columns and giving +# them lowercase names is our processing. The builder is committed -- see +# `builder` below. +class: constructed + +source: + name: Ames Housing data (De Cock 2011), Journal of Statistics Education + url: http://jse.amstat.org/v19n3/decock.pdf + file_url: http://jse.amstat.org/v19n3/decock/AmesHousing.xls + doi: 10.1080/10691898.2011.11889627 + citation: > + De Cock, D. (2011). Ames, Iowa: Alternative to the Boston Housing Data as + an End of Semester Regression Project. Journal of Statistics Education, + 19(3). + +license: + name: null + url: null + redistribution: permitted + verified: 2026-08-03 + # The article states no formal licence. It presents the data as a teaching + # set published for reuse -- an open replacement for the Boston housing data + # -- and distributes the workbook from the journal's own server alongside a + # data-documentation file. Redistribution is long established in practice: + # CRAN ships it in the AmesHousing package and OpenML serves it as dataset + # 42165. Recorded as `permitted` on that basis, with `name: null` because + # there is a genuine licence statement to point at, not because one was not + # looked for. Attribution is carried by the citation above. + +retrieved: 2026-08-03 +maintainer: QuantEcon + +integrity: + sha256: 9878434620e2f295a1e00662197f97ecf87681361a3628802d11697f80aaa4f4 + upstream: + status: verified + date: 2026-08-03 + against: AmesHousing.xls + note: > + Written by re-running the committed builder against the workbook on the + journal's server on the retrieval date, so the file is the builder's + output by construction. The upstream workbook is a fixed 2011 release, + so re-running the builder should reproduce this hash indefinitely. + +schema: + format: csv + columns: + - {name: price, dtype: int64, description: sale price in US dollars} + - {name: bedrooms, dtype: int64, description: bedrooms above grade} + - {name: living_area_sqft, dtype: int64, description: above-grade living area in square feet} + - {name: year_built, dtype: int64, description: year of original construction} + - {name: neighborhood, dtype: object, description: neighborhood within Ames city limits} + row_count_floor: 2930 + date_range: {start: 2006, end: 2010} + known_nulls: {} + +consumers: + - repo: QuantEcon/lecture-python-intro + file: lectures/observed_distributions.md + +builder: scripts/ames_house_prices.py +builder_status: committed diff --git a/lectures/japan_deaths_by_age.csv b/lectures/japan_deaths_by_age.csv new file mode 100644 index 0000000..2f21531 --- /dev/null +++ b/lectures/japan_deaths_by_age.csv @@ -0,0 +1,102 @@ +age,deaths_male,deaths_female,deaths_total +0,672,575,1246 +1,104,106,211 +2,63,60,122 +3,53,47,100 +4,40,42,82 +5,38,39,77 +6,26,32,59 +7,41,29,70 +8,39,22,61 +9,40,23,62 +10,26,23,49 +11,52,33,85 +12,43,33,76 +13,55,45,100 +14,61,47,108 +15,89,64,152 +16,96,64,160 +17,153,76,229 +18,163,94,257 +19,209,106,315 +20,256,119,375 +21,299,138,438 +22,329,153,482 +23,293,147,440 +24,294,135,429 +25,270,139,409 +26,274,148,422 +27,281,170,451 +28,286,136,422 +29,328,157,485 +30,365,162,528 +31,319,178,497 +32,336,196,531 +33,370,196,566 +34,391,230,620 +35,424,258,681 +36,460,279,740 +37,504,280,784 +38,559,310,869 +39,640,343,983 +40,688,403,1090 +41,712,463,1175 +42,770,495,1266 +43,924,530,1454 +44,1002,634,1635 +45,1180,726,1905 +46,1328,797,2125 +47,1620,918,2538 +48,1871,1112,2983 +49,2176,1285,3461 +50,2410,1366,3776 +51,2646,1519,4165 +52,2674,1493,4168 +53,2962,1587,4549 +54,3304,1673,4977 +55,3336,1697,5033 +56,3422,1606,5029 +57,3587,1752,5340 +58,4299,2046,6344 +59,4458,2151,6608 +60,4843,2120,6964 +61,5225,2265,7489 +62,5490,2419,7909 +63,6168,2627,8795 +64,6854,2822,9676 +65,6943,3009,9952 +66,7931,3297,11228 +67,8832,3740,12572 +68,9898,4156,14054 +69,11246,4771,16016 +70,13080,5530,18611 +71,15003,6534,21537 +72,17218,7649,24867 +73,18401,8569,26970 +74,21952,10414,32366 +75,24322,12222,36544 +76,25131,12854,37986 +77,21815,11425,33240 +78,18881,10638,29519 +79,22964,13294,36258 +80,26670,16294,42965 +81,29511,18352,47864 +82,31376,20781,52157 +83,31105,21906,53011 +84,29515,22298,51813 +85,28963,23188,52151 +86,31202,27579,58780 +87,33253,31222,64475 +88,31840,33621,65461 +89,29926,34423,64350 +90,27555,35847,63402 +91,24919,36577,61496 +92,22036,35990,58026 +93,18741,34097,52839 +94,15262,31430,46692 +95,12354,28627,40981 +96,9818,25563,35381 +97,7652,22605,30258 +98,5757,19343,25100 +99,4197,15801,19998 +100,8006,42277,50284 diff --git a/lectures/japan_deaths_by_age.csv.yml b/lectures/japan_deaths_by_age.csv.yml new file mode 100644 index 0000000..238a1e1 --- /dev/null +++ b/lectures/japan_deaths_by_age.csv.yml @@ -0,0 +1,76 @@ +# Manifest for japan_deaths_by_age.csv. + +filename: japan_deaths_by_age.csv +title: Japan — deaths by single year of age, 2023 +description: > + The age distribution of deaths in Japan in 2023: counts by single year of age + from 0 to "100 and over", split by sex. Estimated deaths from the UN World + Population Prospects 2024 revision, medium variant. Used as an example of a + strongly left-skewed observed distribution. + +# Constructed, not verbatim: the upstream release covers every location and +# every year from 1950 in a single 45 MB gzipped CSV. Selecting Japan in 2023, +# scaling the published thousands to persons, and keeping four columns is our +# processing. The builder is committed -- see `builder` below. +class: constructed + +source: + name: > + United Nations, Department of Economic and Social Affairs, Population + Division — World Population Prospects 2024 + series: WPP2024_DeathsBySingleAgeSex_Medium_1950-2023 + url: https://population.un.org/wpp/downloads + file_url: > + https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_DeathsBySingleAgeSex_Medium_1950-2023.csv.gz + version: 2024 revision + citation: > + United Nations, Department of Economic and Social Affairs, Population + Division (2024). World Population Prospects 2024, Online Edition. + +license: + name: CC BY 3.0 IGO + url: https://creativecommons.org/licenses/by/3.0/igo/ + redistribution: permitted + verified: 2026-08-03 + # Established against the statement carried on the WPP download page: + # "Copyright (c) 2024 by United Nations, made available under a Creative + # Commons license CC BY 3.0 IGO: http://creativecommons.org/licenses/by/3.0/igo/". + # Attribution is the sole condition and the suggested citation above -- the + # wording the UN itself supplies -- carries it, so caching this extract and + # serving it publicly is permitted. + +retrieved: 2026-08-03 +maintainer: QuantEcon + +integrity: + sha256: 1e03f6d952efc657f64047b6bebab9cd3f79af193e0468b91d525682ce0dbf09 + upstream: + status: verified + date: 2026-08-03 + against: WPP2024_DeathsBySingleAgeSex_Medium_1950-2023.csv.gz + note: > + Written by re-running the committed builder against the live upstream + release on the retrieval date, so the file is the builder's output by + construction. Re-running it reproduces this hash while the 2024 revision + remains the current WPP vintage. + +schema: + format: csv + columns: + - {name: age, dtype: int64, description: single year of age; 100 means "100 and over"} + - {name: deaths_male, dtype: int64, description: male deaths at this age in 2023} + - {name: deaths_female, dtype: int64, description: female deaths at this age in 2023} + - {name: deaths_total, dtype: int64, description: all deaths at this age in 2023} + row_count_floor: 101 + date_range: {start: 2023, end: 2023} + known_nulls: {} + # Counts are published in thousands to three decimals and are scaled to + # persons and rounded here, so deaths_male + deaths_female may differ from + # deaths_total by one person at a given age. The builder asserts that bound. + +consumers: + - repo: QuantEcon/lecture-python-intro + file: lectures/observed_distributions.md + +builder: scripts/japan_deaths_by_age.py +builder_status: committed diff --git a/lectures/us_adult_heights.csv b/lectures/us_adult_heights.csv new file mode 100644 index 0000000..3937af8 --- /dev/null +++ b/lectures/us_adult_heights.csv @@ -0,0 +1,10479 @@ +sex,height_cm +male,184.5 +male,171.4 +male,170.1 +male,165.4 +male,179.4 +male,176.7 +male,177.8 +male,183.8 +male,170.7 +male,166.1 +male,170.7 +male,182.2 +male,173.9 +male,187.4 +male,166.3 +male,182.2 +male,178.4 +male,183.7 +male,173.8 +male,165.7 +male,180.3 +male,177.5 +male,170.6 +male,176.4 +male,179.7 +male,172.8 +male,170.6 +male,181.5 +male,189.4 +male,175.6 +male,169.3 +male,169.8 +male,170.0 +male,173.9 +male,167.0 +male,175.9 +male,178.7 +male,173.1 +male,185.4 +male,177.0 +male,188.5 +male,172.4 +male,169.1 +male,178.9 +male,171.6 +male,173.6 +male,170.5 +male,174.0 +male,173.3 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centimetres for 10,478 US adults aged 20 and over, + labelled by sex, pooled across the NHANES 2015-2016 and 2017-2018 cycles. + Pregnant respondents are excluded. Used as an example of an observed + distribution that is closely approximated by a normal distribution. + +# Constructed: two survey cycles, each shipping demographics and body measures +# as separate SAS transport files, joined and filtered down to two columns. +class: constructed + +source: + name: > + National Health and Nutrition Examination Survey (NHANES), US Centers for + Disease Control and Prevention, National Center for Health Statistics + series: DEMO_I + BMX_I (2015-2016), DEMO_J + BMX_J (2017-2018) + url: https://www.cdc.gov/nchs/nhanes/index.htm + file_url: https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/DEMO_I.xpt + citation: > + Centers for Disease Control and Prevention (CDC), National Center for + Health Statistics (NCHS). National Health and Nutrition Examination Survey + Data, 2015-2016 and 2017-2018. Hyattsville, MD: U.S. Department of Health + and Human Services. + +license: + name: US Government work — public domain + url: https://www.cdc.gov/other/agencymaterials.html + redistribution: permitted + verified: 2026-08-03 + # NCHS publishes NHANES as public-use data files. CDC states that its + # materials are not copyrighted and may be reproduced without permission, + # with citation requested. Attribution is carried by the citation above. + +retrieved: 2026-08-03 +maintainer: QuantEcon + +# --------------------------------------------------------------------------- +# Integrity +# --------------------------------------------------------------------------- +# Migration check (not a manifest field — a one-time repoint gate): the bytes +# here are identical to lecture-python-intro's copy at +# lectures/_static/lecture_specific/prob_dist/us_adult_heights.csv, so the +# repoint cannot change lecture output. +# +# The CSV was first assembled for lecture-python-intro#790 with no committed +# script — the `builder_status: unrecovered` case AGENTS.md describes. The +# builder was recovered by re-deriving the file from NHANES: the recovered +# script reproduces this sha256 exactly, which both identifies the original +# filters (adults 20+, height recorded, pregnant excluded, males then females) +# and makes the file re-derivable from upstream. Recorded as `committed` +# rather than `unrecovered` on that basis. + +integrity: + sha256: 6240535326bc7e23c1da5540dc3005bda491e47820f31c1f2aa052d9bfb1ae0c + upstream: + status: verified + date: 2026-08-03 + against: DEMO_I.xpt + BMX_I.xpt + DEMO_J.xpt + BMX_J.xpt + note: > + Re-running the committed builder against the NHANES public files + reproduces this file byte for byte. The two cycles are closed releases, + so the hash should hold indefinitely. + +schema: + format: csv + columns: + - {name: sex, dtype: object, description: "male or female (NHANES RIAGENDR)"} + - {name: height_cm, dtype: float64, description: standing height in centimetres (NHANES BMXHT)} + row_count_floor: 10478 + date_range: {start: 2015, end: 2018} + known_nulls: {} + # Rows are grouped: 5,092 male rows followed by 5,386 female rows. + +consumers: + - repo: QuantEcon/lecture-python-intro + file: lectures/prob_dist.md + - repo: QuantEcon/lecture-python-intro + file: lectures/observed_distributions.md + +builder: scripts/us_adult_heights.py +builder_status: committed diff --git a/migration.yml b/migration.yml index 6b68dfb..0e2f8cb 100644 --- a/migration.yml +++ b/migration.yml @@ -80,6 +80,44 @@ datasets: date: 2026-07-17 cutover: null + ames_house_prices.csv: + pilot: P5 + status: landed + # A new dataset, not a migration: these bytes have never lived in a lecture + # repo, so there is no prior pattern to move away from. The consuming + # lecture is new too and lands with the interim raw URL already in place; + # flip to `repointed` when QuantEcon/lecture-python-intro#XXX merges. + prior_pattern: none + landed: + pr: QuantEcon/data-lectures#27 + date: 2026-08-03 + repoints: [] + cutover: null + + japan_deaths_by_age.csv: + pilot: P5 + status: landed + prior_pattern: none + landed: + pr: QuantEcon/data-lectures#27 + date: 2026-08-03 + repoints: [] + cutover: null + + us_adult_heights.csv: + pilot: P5 + status: landed + # Migrated out of lecture-python-intro, where it was added by #790 with no + # committed builder. The builder was recovered during this migration and + # reproduces the file byte for byte, so it lands as `committed` rather than + # the `unrecovered` state Phase 9 tracks. + prior_pattern: local-path + landed: + pr: QuantEcon/data-lectures#27 + date: 2026-08-03 + repoints: [] + cutover: null + # Planned waves that have not landed anything here yet. `datasets` names the # files as the audit sees them today, so the dashboard can join the two views. # `title` is the reader-facing milestone name (the dashboard is read by people diff --git a/scripts/ames_house_prices.py b/scripts/ames_house_prices.py new file mode 100644 index 0000000..e5c7ebc --- /dev/null +++ b/scripts/ames_house_prices.py @@ -0,0 +1,61 @@ +""" +Builder for lectures/ames_house_prices.csv. + +Subsets the Ames, Iowa housing data of De Cock (2011) down to the sale price +and a few characteristics of each house, and gives the columns lowercase names. + +The upstream file is an 82-column Excel workbook assembled for a regression +exercise; the extract here keeps only what a lecture on observed distributions +needs. + +Stages: fetch -> pre-process -> validate -> write. +""" + +import os + +import pandas as pd + +CURRENT_FILE_DIR = os.path.dirname(os.path.abspath(__file__)) +REPO_ROOT = os.path.dirname(CURRENT_FILE_DIR) +PUBLISHED_DIR = os.path.join(REPO_ROOT, 'lectures') + +SOURCE_URL = 'http://jse.amstat.org/v19n3/decock/AmesHousing.xls' + +OUT_FILE = 'ames_house_prices.csv' + +COLUMNS = { + 'SalePrice': 'price', + 'Bedroom AbvGr': 'bedrooms', + 'Gr Liv Area': 'living_area_sqft', + 'Year Built': 'year_built', + 'Neighborhood': 'neighborhood', +} + + +def fetch(): + return pd.read_excel(SOURCE_URL) + + +def pre_process(raw): + return raw[list(COLUMNS)].rename(columns=COLUMNS) + + +def validate(df): + """Refuse to write anything that is not the shape we expect.""" + assert list(df.columns) == list(COLUMNS.values()) + assert len(df) == 2930, f'expected 2930 sales, got {len(df)}' + assert not df.isnull().values.any() + assert df['price'].min() > 0 + assert df['year_built'].between(1800, 2010).all() + + +def run(): + df = pre_process(fetch()) + validate(df) + df.to_csv(os.path.join(PUBLISHED_DIR, OUT_FILE), index=False) + print(f'wrote {OUT_FILE}: {len(df)} rows, ' + f'median price {df["price"].median():,.0f}') + + +if __name__ == '__main__': + run() diff --git a/scripts/japan_deaths_by_age.py b/scripts/japan_deaths_by_age.py new file mode 100644 index 0000000..83a11b7 --- /dev/null +++ b/scripts/japan_deaths_by_age.py @@ -0,0 +1,80 @@ +""" +Builder for lectures/japan_deaths_by_age.csv. + +Extracts the age distribution of deaths in Japan in a single year from the UN +World Population Prospects (WPP) "deaths by single age and sex" release. + +The upstream file covers every location and every year from 1950, and is ~45 MB +gzipped; we stream it in chunks and keep one country-year. Death counts are +published in thousands, so the counts are scaled to persons and rounded to +integers. + +Stages: fetch -> pre-process -> validate -> write. +""" + +import os + +import pandas as pd + +CURRENT_FILE_DIR = os.path.dirname(os.path.abspath(__file__)) +REPO_ROOT = os.path.dirname(CURRENT_FILE_DIR) +PUBLISHED_DIR = os.path.join(REPO_ROOT, 'lectures') + +SOURCE_URL = ( + 'https://population.un.org/wpp/assets/Excel%20Files/' + '1_Indicator%20(Standard)/CSV_FILES/' + 'WPP2024_DeathsBySingleAgeSex_Medium_1950-2023.csv.gz' +) + +ISO3 = 'JPN' +YEAR = 2023 +OUT_FILE = 'japan_deaths_by_age.csv' + +USE_COLS = ['ISO3_code', 'Location', 'Time', 'AgeGrpStart', + 'DeathMale', 'DeathFemale', 'DeathTotal'] + + +def fetch(): + """Stream the upstream release, keeping only the country-year we need.""" + chunks = pd.read_csv(SOURCE_URL, usecols=USE_COLS, + chunksize=500_000, low_memory=False) + keep = [c[(c['ISO3_code'] == ISO3) & (c['Time'] == YEAR)] for c in chunks] + return pd.concat(keep, ignore_index=True) + + +def pre_process(raw): + """Scale thousands to persons and keep age plus the three count columns.""" + df = pd.DataFrame({ + 'age': raw['AgeGrpStart'].astype(int), + 'deaths_male': (raw['DeathMale'] * 1000).round().astype(int), + 'deaths_female': (raw['DeathFemale'] * 1000).round().astype(int), + 'deaths_total': (raw['DeathTotal'] * 1000).round().astype(int), + }) + return df.sort_values('age').reset_index(drop=True) + + +def validate(df): + """Refuse to write anything that is not the shape we expect.""" + assert list(df.columns) == ['age', 'deaths_male', 'deaths_female', + 'deaths_total'] + assert len(df) == 101, f'expected ages 0-100+, got {len(df)} rows' + assert df['age'].tolist() == list(range(101)) + assert not df.isnull().values.any() + assert (df[['deaths_male', 'deaths_female', 'deaths_total']] >= 0).all().all() + # The three counts are rounded independently, so allow a rounding slack. + gap = (df['deaths_male'] + df['deaths_female'] - df['deaths_total']).abs() + assert gap.max() <= 1, f'male + female != total by {gap.max()}' + total = df['deaths_total'].sum() + assert 1_000_000 < total < 2_000_000, f'implausible death total {total}' + + +def run(): + df = pre_process(fetch()) + validate(df) + df.to_csv(os.path.join(PUBLISHED_DIR, OUT_FILE), index=False) + print(f'wrote {OUT_FILE}: {len(df)} rows, ' + f'{df["deaths_total"].sum():,} deaths') + + +if __name__ == '__main__': + run() diff --git a/scripts/us_adult_heights.py b/scripts/us_adult_heights.py new file mode 100644 index 0000000..38ed378 --- /dev/null +++ b/scripts/us_adult_heights.py @@ -0,0 +1,85 @@ +""" +Builder for lectures/us_adult_heights.csv. + +Extracts standing height by sex for US adults from two NHANES cycles, +2015-2016 (suffix I) and 2017-2018 (suffix J). + +Each cycle ships demographics (DEMO) and body measures (BMX) as separate SAS +transport files, joined on the respondent id SEQN. The extract keeps adults +aged 20 and over with a recorded height, excluding pregnant respondents, and +writes males followed by females. + +This builder was recovered after the fact: the CSV was first assembled for +lecture-python-intro#790 without a committed script. Its output is +byte-identical to that file (sha256 6240535326bc7e23...), so the filters below +are the original ones, not a reinterpretation. + +Stages: fetch -> pre-process -> validate -> write. +""" + +import os + +import pandas as pd + +CURRENT_FILE_DIR = os.path.dirname(os.path.abspath(__file__)) +REPO_ROOT = os.path.dirname(CURRENT_FILE_DIR) +PUBLISHED_DIR = os.path.join(REPO_ROOT, 'lectures') + +BASE_URL = 'https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public' + +# (demographics file, body measures file, cycle directory) +CYCLES = [('DEMO_I', 'BMX_I', '2015'), + ('DEMO_J', 'BMX_J', '2017')] + +OUT_FILE = 'us_adult_heights.csv' + +MIN_AGE = 20 # RIDAGEYR — "adult" for this extract +PREGNANT = 1 # RIDEXPRG code for "pregnant at exam" +MALE, FEMALE = 1, 2 # RIAGENDR codes + + +def fetch(): + """Join demographics to body measures, one frame per cycle.""" + frames = [] + for demo, bmx, cycle in CYCLES: + dem = pd.read_sas(f'{BASE_URL}/{cycle}/DataFiles/{demo}.xpt') + bod = pd.read_sas(f'{BASE_URL}/{cycle}/DataFiles/{bmx}.xpt') + frames.append( + dem[['SEQN', 'RIAGENDR', 'RIDAGEYR', 'RIDEXPRG']] + .merge(bod[['SEQN', 'BMXHT']], on='SEQN') + ) + return pd.concat(frames, ignore_index=True) + + +def pre_process(raw): + """Adults with a recorded height, excluding pregnant respondents.""" + adults = raw[(raw['RIDAGEYR'] >= MIN_AGE) + & raw['BMXHT'].notna() + & (raw['RIDEXPRG'] != PREGNANT)] + male = adults[adults['RIAGENDR'] == MALE]['BMXHT'] + female = adults[adults['RIAGENDR'] == FEMALE]['BMXHT'] + return pd.DataFrame({ + 'sex': ['male'] * len(male) + ['female'] * len(female), + 'height_cm': list(male) + list(female), + }) + + +def validate(df): + """Refuse to write anything that is not the shape we expect.""" + assert list(df.columns) == ['sex', 'height_cm'] + assert not df.isnull().values.any() + counts = df['sex'].value_counts() + assert counts['male'] == 5092, f'male count changed: {counts["male"]}' + assert counts['female'] == 5386, f'female count changed: {counts["female"]}' + assert df['height_cm'].between(100, 230).all() + + +def run(): + df = pre_process(fetch()) + validate(df) + df.to_csv(os.path.join(PUBLISHED_DIR, OUT_FILE), index=False) + print(f'wrote {OUT_FILE}: {len(df)} rows') + + +if __name__ == '__main__': + run()