From aa31593c1cd8e451346391a3b4ed3add16eb92f3 Mon Sep 17 00:00:00 2001 From: John Stachurski Date: Mon, 3 Aug 2026 20:10:02 +1000 Subject: [PATCH 1/2] Add count and waiting-time datasets for a distribution-fitting lecture MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two datasets for a lecture in preparation on fitting distributions to data, which needs count data to go with the continuous data already here. - epl_match_goals.csv — full-time scores for ten complete English Premier League seasons, 3,800 matches. Goals per match average 2.830 with variance 2.777, so the Poisson fit is close across the whole range of counts, which is what the lecture needs from a worked example. - japan_earthquakes.csv — 3,557 events of magnitude 5+ around Japan, 2000-2024, from the USGS catalog. This one is the counterexample: monthly counts have a variance-to-mean ratio near 97 and the times between events have a standard deviation half again their mean, because aftershocks cluster. Neither Poisson nor exponential survives contact with it, which makes the point that independence is doing real work. Both are class: constructed and ship builders following the fetch -> pre-process -> validate -> write staging. The football builder asserts exactly 380 matches in each of ten seasons so a partially published season cannot slip in; the earthquake builder asserts the magnitude floor, the bounding box and the year range. Licences are clean: openfootball dedicates its data to the public domain explicitly, and USGS output is a US Government work. The USGS event service is a live API, so per AGENTS.md the builder queries it once over a closed window and commits the result rather than leaving a lecture to call it. Its manifest records that ComCat is a revised catalog, so a later re-run may differ slightly from the committed hash. Consumers are empty in both manifests: the lecture that will read them is still being written, and these records get their entries when it lands. Part of QuantEcon/meta#336 Co-Authored-By: Claude Opus 5 (1M context) --- CATALOG.md | 4 +- lectures/epl_match_goals.csv | 3801 ++++++++++++++++++++++++++++ lectures/epl_match_goals.csv.yml | 70 + lectures/japan_earthquakes.csv | 3558 ++++++++++++++++++++++++++ lectures/japan_earthquakes.csv.yml | 80 + migration.yml | 22 + scripts/epl_match_goals.py | 81 + scripts/japan_earthquakes.py | 83 + 8 files changed, 7698 insertions(+), 1 deletion(-) create mode 100644 lectures/epl_match_goals.csv create mode 100644 lectures/epl_match_goals.csv.yml create mode 100644 lectures/japan_earthquakes.csv create mode 100644 lectures/japan_earthquakes.csv.yml create mode 100644 scripts/epl_match_goals.py create mode 100644 scripts/japan_earthquakes.py diff --git a/CATALOG.md b/CATALOG.md index 742be84..7cc3586 100644 --- a/CATALOG.md +++ b/CATALOG.md @@ -6,14 +6,16 @@ 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. -**7 datasets migrated** · 2.0 MB total · 6 permitted / 1 restricted redistribution +**9 datasets migrated** · 2.4 MB total · 8 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) | +| [**epl_match_goals.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/epl_match_goals.csv)
English Premier League — full-time scores, 2015-16 to 2024-25 | constructed | [openfootball / football.json](https://github.com/openfootball/football.json) | Public domain | ✅ permitted | ✅ verified | ✅ committed | 203.2 KB | — | | [**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) | +| [**japan_earthquakes.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/japan_earthquakes.csv)
Japan region — earthquakes of magnitude 5 and above, 2000-2024 | constructed | [Advanced National Seismic System (ANSS) Comprehensive Earthquake Catalog (ComCat), US Geological Survey](https://earthquake.usgs.gov/earthquakes/search/) | US Government work — public domain | ✅ permitted | ✅ verified | ✅ committed | 172.8 KB | — | | [**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/epl_match_goals.csv b/lectures/epl_match_goals.csv new file mode 100644 index 0000000..444d744 --- /dev/null +++ b/lectures/epl_match_goals.csv @@ -0,0 +1,3801 @@ +season,date,home_team,away_team,home_goals,away_goals +2015-16,2015-08-08,Manchester United,Tottenham Hotspur,1,0 +2015-16,2015-08-08,AFC Bournemouth,Aston Villa,0,1 +2015-16,2015-08-08,Everton FC,Watford FC,2,2 +2015-16,2015-08-08,Leicester City,Sunderland AFC,4,2 +2015-16,2015-08-08,Norwich City,Crystal Palace,1,3 +2015-16,2015-08-08,Chelsea FC,Swansea City,2,2 +2015-16,2015-08-09,Newcastle United,Southampton FC,2,2 +2015-16,2015-08-09,Stoke City,Liverpool FC,0,1 +2015-16,2015-08-09,Arsenal FC,West Ham United,0,2 +2015-16,2015-08-10,West Bromwich Albion,Manchester City,0,3 +2015-16,2015-08-14,Aston Villa,Manchester United,0,1 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Hove Albion FC,3,0 +2021-22,2022-04-21,Burnley FC,Southampton FC,2,0 +2021-22,2022-04-23,Arsenal FC,Manchester United FC,3,1 +2021-22,2022-04-23,Leicester City FC,Aston Villa FC,0,0 +2021-22,2022-04-23,Manchester City FC,Watford FC,5,1 +2021-22,2022-04-23,Norwich City FC,Newcastle United FC,0,3 +2021-22,2022-04-23,Brentford FC,Tottenham Hotspur FC,0,0 +2021-22,2022-04-24,Brighton & Hove Albion FC,Southampton FC,2,2 +2021-22,2022-04-24,Burnley FC,Wolverhampton Wanderers FC,1,0 +2021-22,2022-04-24,Chelsea FC,West Ham United FC,1,0 +2021-22,2022-04-24,Liverpool FC,Everton FC,2,0 +2021-22,2022-04-25,Crystal Palace FC,Leeds United FC,0,0 +2021-22,2022-04-28,Manchester United FC,Chelsea FC,1,1 +2021-22,2022-04-30,Newcastle United FC,Liverpool FC,0,1 +2021-22,2022-04-30,Aston Villa FC,Norwich City FC,2,0 +2021-22,2022-04-30,Southampton FC,Crystal Palace FC,1,2 +2021-22,2022-04-30,Watford FC,Burnley FC,1,2 +2021-22,2022-04-30,Wolverhampton Wanderers FC,Brighton & Hove Albion FC,0,3 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United FC,Manchester City FC,0,2 +2022-23,2022-08-13,Aston Villa FC,Everton FC,2,1 +2022-23,2022-08-13,Wolverhampton Wanderers FC,Fulham FC,0,0 +2022-23,2022-08-13,Southampton FC,Leeds United FC,2,2 +2022-23,2022-08-13,Manchester City FC,AFC Bournemouth,4,0 +2022-23,2022-08-13,Brighton & Hove Albion FC,Newcastle United FC,0,0 +2022-23,2022-08-13,Arsenal FC,Leicester City FC,4,2 +2022-23,2022-08-13,Brentford FC,Manchester United FC,4,0 +2022-23,2022-08-14,Nottingham Forest FC,West Ham United FC,1,0 +2022-23,2022-08-14,Chelsea FC,Tottenham Hotspur FC,2,2 +2022-23,2022-08-15,Liverpool FC,Crystal Palace FC,1,1 +2022-23,2022-08-20,Tottenham Hotspur FC,Wolverhampton Wanderers FC,1,0 +2022-23,2022-08-20,Leicester City FC,Southampton FC,1,2 +2022-23,2022-08-20,Fulham FC,Brentford FC,3,2 +2022-23,2022-08-20,Everton FC,Nottingham Forest FC,1,1 +2022-23,2022-08-20,Crystal Palace FC,Aston Villa FC,3,1 +2022-23,2022-08-20,AFC Bournemouth,Arsenal FC,0,3 +2022-23,2022-08-21,West Ham United FC,Brighton & Hove Albion FC,0,2 +2022-23,2022-08-21,Leeds United FC,Chelsea FC,3,0 +2022-23,2022-08-21,Newcastle United FC,Manchester City FC,3,3 +2022-23,2022-08-22,Manchester United FC,Liverpool FC,2,1 +2022-23,2022-08-27,Southampton FC,Manchester United FC,0,1 +2022-23,2022-08-27,Manchester City FC,Crystal Palace FC,4,2 +2022-23,2022-08-27,Liverpool FC,AFC Bournemouth,9,0 +2022-23,2022-08-27,Chelsea FC,Leicester City FC,2,1 +2022-23,2022-08-27,Brighton & Hove Albion FC,Leeds United FC,1,0 +2022-23,2022-08-27,Brentford FC,Everton FC,1,1 +2022-23,2022-08-27,Arsenal FC,Fulham FC,2,1 +2022-23,2022-08-28,Wolverhampton Wanderers FC,Newcastle United FC,1,1 +2022-23,2022-08-28,Aston Villa FC,West Ham United FC,0,1 +2022-23,2022-08-28,Nottingham Forest FC,Tottenham Hotspur FC,0,2 +2022-23,2022-08-30,Crystal Palace FC,Brentford FC,1,1 +2022-23,2022-08-30,Fulham FC,Brighton & Hove Albion FC,2,1 +2022-23,2022-08-30,Southampton FC,Chelsea FC,2,1 +2022-23,2022-08-30,Leeds United FC,Everton FC,1,1 +2022-23,2022-08-31,Manchester City FC,Nottingham Forest FC,6,0 +2022-23,2022-08-31,AFC Bournemouth,Wolverhampton Wanderers FC,0,0 +2022-23,2022-08-31,Arsenal FC,Aston Villa FC,2,1 +2022-23,2022-08-31,West Ham United FC,Tottenham Hotspur FC,1,1 +2022-23,2022-08-31,Liverpool FC,Newcastle United FC,2,1 +2022-23,2022-09-01,Leicester City FC,Manchester United FC,0,1 +2022-23,2022-09-03,Everton FC,Liverpool FC,0,0 +2022-23,2022-09-03,Wolverhampton Wanderers FC,Southampton FC,1,0 +2022-23,2022-09-03,Tottenham Hotspur FC,Fulham FC,2,1 +2022-23,2022-09-03,Nottingham Forest FC,AFC Bournemouth,2,3 +2022-23,2022-09-03,Newcastle United FC,Crystal Palace FC,0,0 +2022-23,2022-09-03,Chelsea FC,West Ham United FC,2,1 +2022-23,2022-09-03,Brentford FC,Leeds United FC,5,2 +2022-23,2022-09-03,Aston Villa FC,Manchester City FC,1,1 +2022-23,2022-09-04,Brighton & Hove Albion FC,Leicester City FC,5,2 +2022-23,2022-09-04,Manchester United FC,Arsenal FC,3,1 +2022-23,2022-09-16,Nottingham Forest FC,Fulham FC,2,3 +2022-23,2022-09-16,Aston Villa FC,Southampton FC,1,0 +2022-23,2022-09-17,Wolverhampton Wanderers FC,Manchester City FC,0,3 +2022-23,2022-09-17,Newcastle United FC,AFC Bournemouth,1,1 +2022-23,2022-09-17,Tottenham Hotspur FC,Leicester City FC,6,2 +2022-23,2022-09-18,Brentford FC,Arsenal FC,0,3 +2022-23,2022-09-18,Everton FC,West Ham United FC,1,0 +2022-23,2022-10-01,Arsenal FC,Tottenham Hotspur FC,3,1 +2022-23,2022-10-01,Southampton FC,Everton FC,1,2 +2022-23,2022-10-01,Liverpool FC,Brighton & Hove Albion FC,3,3 +2022-23,2022-10-01,Fulham FC,Newcastle United FC,1,4 +2022-23,2022-10-01,Crystal Palace FC,Chelsea FC,1,2 +2022-23,2022-10-01,AFC Bournemouth,Brentford FC,0,0 +2022-23,2022-10-01,West Ham United FC,Wolverhampton Wanderers FC,2,0 +2022-23,2022-10-02,Manchester City FC,Manchester United FC,6,3 +2022-23,2022-10-02,Leeds United FC,Aston Villa FC,0,0 +2022-23,2022-10-03,Leicester City FC,Nottingham Forest FC,4,0 +2022-23,2022-10-08,Newcastle United FC,Brentford FC,5,1 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FC,Brentford FC,4,0 +2022-23,2022-10-23,Wolverhampton Wanderers FC,Leicester City FC,0,4 +2022-23,2022-10-23,Tottenham Hotspur FC,Newcastle United FC,1,2 +2022-23,2022-10-24,West Ham United FC,AFC Bournemouth,2,0 +2022-23,2022-10-29,Leicester City FC,Manchester City FC,0,1 +2022-23,2022-10-29,Newcastle United FC,Aston Villa FC,4,0 +2022-23,2022-10-29,Crystal Palace FC,Southampton FC,1,0 +2022-23,2022-10-29,Brighton & Hove Albion FC,Chelsea FC,4,1 +2022-23,2022-10-29,Brentford FC,Wolverhampton Wanderers FC,1,1 +2022-23,2022-10-29,AFC Bournemouth,Tottenham Hotspur FC,2,3 +2022-23,2022-10-29,Fulham FC,Everton FC,0,0 +2022-23,2022-10-29,Liverpool FC,Leeds United FC,1,2 +2022-23,2022-10-30,Arsenal FC,Nottingham Forest FC,5,0 +2022-23,2022-10-30,Manchester United FC,West Ham United FC,1,0 +2022-23,2022-11-05,Wolverhampton Wanderers FC,Brighton & Hove Albion FC,2,3 +2022-23,2022-11-05,Nottingham Forest FC,Brentford FC,2,2 +2022-23,2022-11-05,Manchester City FC,Fulham FC,2,1 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Forest FC,Chelsea FC,0,1 +2024-25,2025-05-25,Southampton FC,Arsenal FC,1,2 +2024-25,2025-05-25,Tottenham Hotspur FC,Brighton & Hove Albion FC,1,4 +2024-25,2025-05-25,Wolverhampton Wanderers FC,Brentford FC,1,1 diff --git a/lectures/epl_match_goals.csv.yml b/lectures/epl_match_goals.csv.yml new file mode 100644 index 0000000..be16f17 --- /dev/null +++ b/lectures/epl_match_goals.csv.yml @@ -0,0 +1,70 @@ +# Manifest for epl_match_goals.csv. + +filename: epl_match_goals.csv +title: English Premier League — full-time scores, 2015-16 to 2024-25 +description: > + Date, teams and full-time score for all 3,800 matches of ten completed + English Premier League seasons. Used as an example of count data that is + well described by a Poisson distribution: goals per match average 2.83 with + a variance of 2.78. + +# Constructed: upstream publishes one JSON file per league-season. Selecting +# ten seasons of one league, flattening the nested score records and keeping +# six fields is our processing. +class: constructed + +source: + name: openfootball / football.json + url: https://github.com/openfootball/football.json + file_url: > + https://raw.githubusercontent.com/openfootball/football.json/master/2023-24/en.1.json + series: en.1 (English Premier League), seasons 2015-16 through 2024-25 + citation: > + openfootball, football.json — free open public domain football match + fixtures and results. + +license: + name: Public domain + url: https://github.com/openfootball/football.json#license + redistribution: permitted + verified: 2026-08-03 + # The upstream README states: "The football.json schema, data and scripts are + # dedicated to the public domain. Use as you please with no restrictions + # whatsoever." No attribution is required; the citation above is courtesy. + +retrieved: 2026-08-03 +maintainer: QuantEcon + +integrity: + sha256: 9afc81ff468f923bf4221dc547db7e7d744d6e3a0eaafc61265ef4c4d5265712 + upstream: + status: verified + date: 2026-08-03 + against: openfootball/football.json en.1, ten season files + note: > + Written by re-running the committed builder against upstream on the + retrieval date. All ten seasons are complete and closed, so re-running + should reproduce this hash unless upstream corrects a historical score. + +schema: + format: csv + columns: + - {name: season, dtype: object, description: "season label, e.g. 2023-24"} + - {name: date, dtype: object, description: match date, ISO 8601} + - {name: home_team, dtype: object, description: home team name} + - {name: away_team, dtype: object, description: away team name} + - {name: home_goals, dtype: int64, description: full-time goals scored by the home team} + - {name: away_goals, dtype: int64, description: full-time goals scored by the away team} + row_count_floor: 3800 + date_range: {start: 2015, end: 2025} + known_nulls: {} + # The builder asserts exactly 380 matches in each of the ten seasons, so a + # partially published season cannot slip in. + +consumers: [] +# The consuming lecture (a distribution-fitting lecture for +# lecture-python-intro) is in preparation; this record gets its entry when that +# lecture lands. + +builder: scripts/epl_match_goals.py +builder_status: committed diff --git a/lectures/japan_earthquakes.csv b/lectures/japan_earthquakes.csv new file mode 100644 index 0000000..740a0fb --- /dev/null +++ b/lectures/japan_earthquakes.csv @@ -0,0 +1,3558 @@ +time,magnitude,latitude,longitude,depth_km +2000-01-09T04:02:23.680Z,5.4,37.28,141.515,56.0 +2000-01-10T16:40:42.240Z,5.7,27.35,139.979,453.2 +2000-01-11T23:43:56.450Z,5.1,40.498,122.994,10.0 +2000-01-13T18:52:12.030Z,5.2,44.393,149.492,48.9 +2000-01-23T07:40:04.900Z,5.5,30.239,130.768,42.9 +2000-01-27T10:10:57.250Z,5.3,31.678,141.686,33.0 +2000-01-28T14:21:07.340Z,6.8,43.046,146.837,61.1 +2000-01-28T16:39:24.280Z,6.0,26.076,124.496,193.9 +2000-02-07T06:34:49.670Z,5.2,43.368,147.433,61.5 +2000-02-07T16:41:04.580Z,5.4,31.037,141.694,33.0 +2000-02-11T07:08:38.870Z,5.1,39.047,140.781,33.0 +2000-02-13T02:57:08.720Z,6.0,42.853,131.572,513.6 +2000-02-14T20:22:23.660Z,5.3,31.04,141.622,33.0 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Used as a counterexample when fitting Poisson + and exponential distributions: aftershocks cluster, so counts per period are + heavily overdispersed and the times between events are not exponential. + +# Constructed: a single closed query against the USGS event service, keeping +# five of its twenty-two columns. The query window is fixed rather than +# rolling, so this is not a dynamic snapshot and has no refresh cadence. +class: constructed + +source: + name: > + Advanced National Seismic System (ANSS) Comprehensive Earthquake Catalog + (ComCat), US Geological Survey + url: https://earthquake.usgs.gov/earthquakes/search/ + file_url: https://earthquake.usgs.gov/fdsnws/event/1/query + series: > + FDSN event query — format=csv, starttime=2000-01-01, endtime=2024-12-31, + minmagnitude=5, minlatitude=24, maxlatitude=46, minlongitude=122, + maxlongitude=150 + citation: > + U.S. Geological Survey, Earthquake Hazards Program (2026). Advanced + National Seismic System (ANSS) Comprehensive Catalog of Earthquake Events + and Products. https://doi.org/10.5066/F7MS3QZH + doi: 10.5066/F7MS3QZH + +license: + name: US Government work — public domain + url: https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits + redistribution: permitted + verified: 2026-08-03 + # USGS states that information produced by the agency is in the public domain + # and may be used without permission, with credit requested. Attribution is + # carried by the citation above. + +retrieved: 2026-08-03 +maintainer: QuantEcon + +integrity: + sha256: ceb80a2aafb322a5d0f2a28d62aa73dd66fa8d6ef353f957bc34cb2bb118048f + upstream: + status: verified + date: 2026-08-03 + against: USGS FDSN event service, query recorded under source.series + note: > + Written by re-running the committed builder against the live service on + the retrieval date. The query window is closed, but ComCat is a revised + catalog -- magnitudes are refined and events are occasionally added or + removed retrospectively -- so a later re-run may differ slightly from + this hash without either version being wrong. That is the reason for + snapshotting it here rather than querying the service from a lecture. + +schema: + format: csv + columns: + - {name: time, dtype: object, description: event origin time, ISO 8601 UTC} + - {name: magnitude, dtype: float64, description: reported magnitude (mixed scales, as ComCat supplies)} + - {name: latitude, dtype: float64, description: epicentre latitude, degrees north} + - {name: longitude, dtype: float64, description: epicentre longitude, degrees east} + - {name: depth_km, dtype: float64, description: hypocentre depth in kilometres} + row_count_floor: 3557 + date_range: {start: 2000, end: 2024} + known_nulls: {} + # This snapshot has no nulls in any column. The builder asserts non-null only + # for time and magnitude, since ComCat can leave depth unconstrained for a + # poorly located event and that should not block a refresh. + +consumers: [] +# The consuming lecture (a distribution-fitting lecture for +# lecture-python-intro) is in preparation; this record gets its entry when that +# lecture lands. + +builder: scripts/japan_earthquakes.py +builder_status: committed diff --git a/migration.yml b/migration.yml index 7b725e7..b13b84f 100644 --- a/migration.yml +++ b/migration.yml @@ -127,6 +127,28 @@ datasets: date: 2026-08-03 cutover: null + epl_match_goals.csv: + pilot: P5 + status: landed + # New dataset with no prior life in a lecture repo. Its consuming lecture + # is still in preparation, so this stays `landed` until that PR merges. + prior_pattern: none + landed: + pr: QuantEcon/data-lectures#XXX + date: 2026-08-03 + repoints: [] + cutover: null + + japan_earthquakes.csv: + pilot: P5 + status: landed + prior_pattern: none + landed: + pr: QuantEcon/data-lectures#XXX + 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/epl_match_goals.py b/scripts/epl_match_goals.py new file mode 100644 index 0000000..7733377 --- /dev/null +++ b/scripts/epl_match_goals.py @@ -0,0 +1,81 @@ +""" +Builder for lectures/epl_match_goals.csv. + +Collects full-time scores for ten completed English Premier League seasons, +2015-16 through 2024-25, from the openfootball football.json release. + +Upstream publishes one JSON file per league-season. The extract keeps the date, +the two teams and the two full-time scores, which is what a lecture fitting a +Poisson distribution to goals per match needs. + +Stages: fetch -> pre-process -> validate -> write. +""" + +import json +import os +import urllib.request + +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://raw.githubusercontent.com/openfootball/football.json/' + 'master/{season}/en.1.json') + +SEASONS = [f'{year}-{str(year + 1)[2:]}' for year in range(2015, 2025)] + +OUT_FILE = 'epl_match_goals.csv' + +MATCHES_PER_SEASON = 380 # 20 teams, home and away + + +def fetch(): + """One JSON file per season.""" + return {s: json.load(urllib.request.urlopen(BASE_URL.format(season=s))) + for s in SEASONS} + + +def pre_process(raw): + """Flatten to one row per match, dropping any match without a full-time score.""" + rows = [] + for season, payload in raw.items(): + for match in payload['matches']: + full_time = (match.get('score') or {}).get('ft') + if full_time is None: + continue + rows.append({ + 'season': season, + 'date': match['date'], + 'home_team': match['team1'], + 'away_team': match['team2'], + 'home_goals': full_time[0], + 'away_goals': full_time[1], + }) + return pd.DataFrame(rows).sort_values(['season', 'date']).reset_index(drop=True) + + +def validate(df): + """Refuse to write anything that is not the shape we expect.""" + assert list(df.columns) == ['season', 'date', 'home_team', 'away_team', + 'home_goals', 'away_goals'] + assert not df.isnull().values.any() + counts = df['season'].value_counts() + assert len(counts) == len(SEASONS), f'expected {len(SEASONS)} seasons' + assert (counts == MATCHES_PER_SEASON).all(), f'incomplete season:\n{counts}' + assert df[['home_goals', 'away_goals']].min().min() >= 0 + assert df[['home_goals', 'away_goals']].max().max() < 15 + + +def run(): + df = pre_process(fetch()) + validate(df) + df.to_csv(os.path.join(PUBLISHED_DIR, OUT_FILE), index=False) + goals = df['home_goals'] + df['away_goals'] + print(f'wrote {OUT_FILE}: {len(df)} matches, ' + f'mean {goals.mean():.3f} goals per match') + + +if __name__ == '__main__': + run() diff --git a/scripts/japan_earthquakes.py b/scripts/japan_earthquakes.py new file mode 100644 index 0000000..b790f97 --- /dev/null +++ b/scripts/japan_earthquakes.py @@ -0,0 +1,83 @@ +""" +Builder for lectures/japan_earthquakes.csv. + +Snapshots the USGS earthquake catalog for events of magnitude 5 and above in +the region around Japan, from 2000 to the end of 2024. + +The USGS FDSN event service is a live API. Following the rule in AGENTS.md +that lectures read snapshots rather than calling APIs, this builder queries it +once and commits the result. + +The extract supports two exercises: whether counts per period look Poisson, and +whether the times between events look exponential. Neither holds, because +aftershocks cluster -- which is the point. + +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') + +# Region: a box around the Japanese archipelago and its subduction zones. +QUERY = { + 'format': 'csv', + 'starttime': '2000-01-01', + 'endtime': '2024-12-31', + 'minmagnitude': '5', + 'minlatitude': '24', + 'maxlatitude': '46', + 'minlongitude': '122', + 'maxlongitude': '150', + 'orderby': 'time-asc', +} + +SOURCE_URL = 'https://earthquake.usgs.gov/fdsnws/event/1/query' + +OUT_FILE = 'japan_earthquakes.csv' + + +def fetch(): + query = '&'.join(f'{k}={v}' for k, v in QUERY.items()) + return pd.read_csv(f'{SOURCE_URL}?{query}') + + +def pre_process(raw): + df = pd.DataFrame({ + 'time': raw['time'], + 'magnitude': raw['mag'], + 'latitude': raw['latitude'], + 'longitude': raw['longitude'], + 'depth_km': raw['depth'], + }) + return df.sort_values('time').reset_index(drop=True) + + +def validate(df): + """Refuse to write anything that is not the shape we expect.""" + assert list(df.columns) == ['time', 'magnitude', 'latitude', 'longitude', + 'depth_km'] + assert not df[['time', 'magnitude']].isnull().values.any() + assert len(df) > 3000, f'suspiciously few events: {len(df)}' + assert df['magnitude'].min() >= 5.0 + assert df['latitude'].between(24, 46).all() + assert df['longitude'].between(122, 150).all() + assert df['time'].is_monotonic_increasing + years = pd.to_datetime(df['time'], format='ISO8601').dt.year + assert years.min() == 2000 and years.max() == 2024 + + +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)} events, ' + f'magnitudes {df["magnitude"].min()} to {df["magnitude"].max()}') + + +if __name__ == '__main__': + run() From 41fc66c1f414fd2fefbe4501923f5a405f84ad30 Mon Sep 17 00:00:00 2001 From: John Stachurski Date: Mon, 3 Aug 2026 20:11:10 +1000 Subject: [PATCH 2/2] migration.yml: record the landing PR number Co-Authored-By: Claude Opus 5 (1M context) --- migration.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/migration.yml b/migration.yml index b13b84f..6b31464 100644 --- a/migration.yml +++ b/migration.yml @@ -134,7 +134,7 @@ datasets: # is still in preparation, so this stays `landed` until that PR merges. prior_pattern: none landed: - pr: QuantEcon/data-lectures#XXX + pr: QuantEcon/data-lectures#31 date: 2026-08-03 repoints: [] cutover: null @@ -144,7 +144,7 @@ datasets: status: landed prior_pattern: none landed: - pr: QuantEcon/data-lectures#XXX + pr: QuantEcon/data-lectures#31 date: 2026-08-03 repoints: [] cutover: null