From f1ef0e7c50c318ecbd3c847444e289857db76427 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Thu, 16 Jul 2026 09:53:02 +1000 Subject: [PATCH] Read the SCF sample from high_dim_data main MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Repoints the SCF+ wealth sample off the update_scf_noweights branch and onto main. This lecture has fetched its data from an unmerged PR's branch since 2023 — a build pinned to a non-default ref, which any branch cleanup would silently break, and which is why that branch cannot be deleted today. Uses the github.com/QuantEcon/high_dim_data/raw/main/… form rather than media.githubusercontent.com. Both serve this LFS-tracked file, but only /raw/ keeps working if the file ever stops being LFS — which it will when the data consolidation folds high_dim_data into data-lectures, a repo that deliberately drops high_dim_data's blanket "*.csv filter=lfs" rule. Repointing to /raw/ now means this URL does not have to change again then. The URL form is verified against the sibling SCF_plus_mini.csv, which is already on main and LFS-tracked: /raw/main/ serves it as real CSV, while raw.githubusercontent.com serves LFS pointer text for the same file. Blocked until QuantEcon/high_dim_data#6 merges — the data file exists only on the branch, so this URL 404s until then. That PR has been open, mergeable and conflict-free since 2023-04-28. Part of QuantEcon/meta#337 See QuantEcon/meta#336 Co-Authored-By: Claude Fable 5 --- lectures/mle.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lectures/mle.md b/lectures/mle.md index 8a15d6ac1..45d641b14 100644 --- a/lectures/mle.md +++ b/lectures/mle.md @@ -90,7 +90,7 @@ The following code imports this data and reads it into an array called `sample` ```{code-cell} ipython3 :tags: [hide-input] -url = 'https://media.githubusercontent.com/media/QuantEcon/high_dim_data/update_scf_noweights/SCF_plus/SCF_plus_mini_no_weights.csv' +url = 'https://github.com/QuantEcon/high_dim_data/raw/main/SCF_plus/SCF_plus_mini_no_weights.csv' df = pd.read_csv(url) df = df.dropna() df = df[df['year'] == 2016]