Record the fitting_distributions lecture as a consumer

CATALOG.md

@@ -10,15 +10,15 @@ The migrated-dataset registry, **auto-generated** from the sidecar manifests (`l
 
 | 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)<br><sub>Ames, Iowa — residential house sales, 2006-2010</sub> | 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) |
+| [**ames_house_prices.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/ames_house_prices.csv)<br><sub>Ames, Iowa — residential house sales, 2006-2010</sub> | 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)<br>[lecture-python-intro · fitting_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/fitting_distributions.md) |
 | [**countries.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/countries.csv)<br><sub>WorldData.info country reference table</sub> | 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)<br>[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)<br><sub>Eurostat employment in Europe — by age and sex, 2007–2016</sub> | 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)<br>[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)<br><sub>English Premier League — full-time scores, 2015-16 to 2024-25</sub> | 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)<br><sub>Japan — deaths by single year of age, 2023</sub> | 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)<br><sub>Japan region — earthquakes of magnitude 5 and above, 2000-2024</sub> | 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 | — |
+| [**epl_match_goals.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/epl_match_goals.csv)<br><sub>English Premier League — full-time scores, 2015-16 to 2024-25</sub> | constructed | [openfootball / football.json](https://github.com/openfootball/football.json) | Public domain | ✅ permitted | ✅ verified | ✅ committed | 203.2 KB | [lecture-python-intro · fitting_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/fitting_distributions.md) |
+| [**japan_deaths_by_age.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/japan_deaths_by_age.csv)<br><sub>Japan — deaths by single year of age, 2023</sub> | 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)<br>[lecture-python-intro · fitting_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/fitting_distributions.md) |
+| [**japan_earthquakes.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/japan_earthquakes.csv)<br><sub>Japan region — earthquakes of magnitude 5 and above, 2000-2024</sub> | 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 | [lecture-python-intro · fitting_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/fitting_distributions.md) |
 | [**lingcod_msy_recovery.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/lingcod_msy_recovery.csv)<br><sub>Pacific Coast lingcod — biomass and fishing pressure relative to MSY</sub> | 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)<br><sub>OECD real minimum wages — 32 countries, 2006–2016</sub> | 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)<br>[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)<br><sub>United States — adult standing height by sex, NHANES 2015-2018</sub> | 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)<br>[lecture-python-intro · observed_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/observed_distributions.md) |
+| [**us_adult_heights.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/us_adult_heights.csv)<br><sub>United States — adult standing height by sex, NHANES 2015-2018</sub> | 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)<br>[lecture-python-intro · observed_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/observed_distributions.md)<br>[lecture-python-intro · fitting_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/fitting_distributions.md) |
 
 ---
 

lectures/ames_house_prices.csv.yml

@@ -68,6 +68,8 @@ schema:
 consumers:
   - repo: QuantEcon/lecture-python-intro
     file: lectures/observed_distributions.md
+  - repo: QuantEcon/lecture-python-intro
+    file: lectures/fitting_distributions.md
 
 builder: scripts/ames_house_prices.py
 builder_status: committed

lectures/epl_match_goals.csv.yml

@@ -61,10 +61,9 @@ schema:
   # 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.
+consumers:
+  - repo: QuantEcon/lecture-python-intro
+    file: lectures/fitting_distributions.md
 
 builder: scripts/epl_match_goals.py
 builder_status: committed

lectures/japan_deaths_by_age.csv.yml

@@ -71,6 +71,8 @@ schema:
 consumers:
   - repo: QuantEcon/lecture-python-intro
     file: lectures/observed_distributions.md
+  - repo: QuantEcon/lecture-python-intro
+    file: lectures/fitting_distributions.md
 
 builder: scripts/japan_deaths_by_age.py
 builder_status: committed

lectures/japan_earthquakes.csv.yml

@@ -71,10 +71,9 @@ schema:
   # 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.
+consumers:
+  - repo: QuantEcon/lecture-python-intro
+    file: lectures/fitting_distributions.md
 
 builder: scripts/japan_earthquakes.py
 builder_status: committed

lectures/us_adult_heights.csv.yml

@@ -79,6 +79,8 @@ consumers:
     file: lectures/prob_dist.md
   - repo: QuantEcon/lecture-python-intro
     file: lectures/observed_distributions.md
+  - repo: QuantEcon/lecture-python-intro
+    file: lectures/fitting_distributions.md
 
 builder: scripts/us_adult_heights.py
 builder_status: committed

migration.yml

@@ -129,24 +129,30 @@ datasets:
 
   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.
+    status: repointed
+    # New dataset with no prior life in a lecture repo, landed ahead of the
+    # lecture that reads it.
     prior_pattern: none
     landed:
       pr: QuantEcon/data-lectures#31
       date: 2026-08-03
-    repoints: []
+    repoints:
+      - repo: QuantEcon/lecture-python-intro
+        pr: QuantEcon/lecture-python-intro#814
+        date: 2026-08-04
     cutover: null
 
   japan_earthquakes.csv:
     pilot: P5
-    status: landed
+    status: repointed
     prior_pattern: none
     landed:
       pr: QuantEcon/data-lectures#31
       date: 2026-08-03
-    repoints: []
+    repoints:
+      - repo: QuantEcon/lecture-python-intro
+        pr: QuantEcon/lecture-python-intro#814
+        date: 2026-08-04
     cutover: null
 
 # Planned waves that have not landed anything here yet. `datasets` names the

scripts/audit_annotations.yml

@@ -179,6 +179,12 @@ api:
     series: SI.POV.GINI, NY.GDP.PCAP.KD
     pedagogy: mixed
     note: demonstrates searching for the Gini series ID with wbgapi
+  lecture-python-intro:fitting_distributions:yfinance:
+    series: AMZN
+    pedagogy: incidental
+    note: >
+      one download, for the section showing that a normal fit to monthly
+      returns fails in the tails
   lecture-python-intro:observed_distributions:yfinance:
     series: AMZN, COST
     pedagogy: incidental