migrate-explorer-csv

name	migrate-explorer-csv
description	Migrate a non-ETL CSV-based explorer (data lives in a static CSV hosted in `owid-grapher/public/explorers/` or on GitHub; explorer config has `tableSlug`/`table` blocks) into ETL by adding a snapshot → meadow → garden → grapher chain, then handing off to `/create-explorer` for the export step. Trigger when the user says "bring CSV explorer <slug> into ETL", or refers to fish-stocks (the only remaining target).
metadata	{"internal":true}

Migrate CSV-based Explorer into ETL

Bring an explorer that today reads from a static CSV (e.g. hosted under owid-grapher/public/explorers/<slug>/... or a GitHub raw URL) into ETL. After this skill, the data is snapshot-tracked and flows through standard ETL stages, and /create-explorer writes the final export://explorers/<ns>/latest/<short> step.

Scope of this skill: the data-pipeline half — locating the source CSV and producing the snapshot → meadow → garden → grapher chain. The export-step authoring (YAML schema, dimensions/views, full-YAML vs table-driven, FAUST upstream, post-processing, DAG, verification) lives in /create-explorer and is shared with the other migrate-explorer-* skills and with /create-multidim. Don't duplicate that content here.

Inputs

Required:

• <slug> — explorer slug (matches the slug column in MySQL explorers)
• CSV source URL or repo path
• <ns> — target namespace under snapshots/ and etl/steps/data/
• <short> — short name for all stages (Python files use snake_case; explorer slug keeps hyphens)

Step 0 — Confirm it really is CSV-based

CSV-based explorers have table and columns blocks alongside the graphers block, with a tableSlug column in the graphers table. Quick check:

cat <<'EOF' > /tmp/classify_explorer.py
from etl.config import OWID_ENV
import json, sys
slug = sys.argv[1]
df = OWID_ENV.read_sql(f"SELECT config FROM explorers WHERE slug = '{slug}'")
cfg = json.loads(df["config"].iloc[0]) if isinstance(df["config"].iloc[0], str) else df["config"].iloc[0]
print("blocks:", [b.get("type") for b in cfg.get("blocks", [])])
EOF
.venv/bin/python /tmp/classify_explorer.py <slug>

Block types should include table and tableSlug should appear in the graphers columns. If you see only graphers (with yVariableIds or grapherId), use migrate-explorer-grapher instead. As of mid-2026 the only remaining CSV explorer is fish-stocks — most of the others (poverty/inequality variants, etc.) are slated for removal per umbrella issue #6014.

Step 1 — Locate the source CSV

Look first in owid-grapher/public/explorers/<slug>/... (the live site path). Otherwise fetch from the URL the explorer points at. Record:

• exact URL (becomes url_main / url_download in the snapshot DVC)
• date last modified — if the CSV is in owid-grapher, use git log -1 --format=%ad path/to/csv to find the latest update; format as YYYY-MM-DD for the snapshot version
• citation / license / source metadata (often in a sibling README or in the explorer's settings rows)

Step 2 — Create the snapshot

Use the create-snapshot skill (or hand-write snapshots/<ns>/<date>/<short>.csv.dvc plus the matching script). The DVC file should record url_main, url_download, date_published, license, citation. Run .venv/bin/etls <ns>/<date>/<short>.csv to fetch and upload.

Step 3 — Meadow step

etl/steps/data/meadow/<ns>/<date>/<short>.py:

from etl.helpers import PathFinder

paths = PathFinder(__file__)


def run() -> None:
    snap = paths.load_snapshot()
    tb = snap.read_csv()
    # Minimal cleaning here; let garden do harmonisation.
    tb = tb.format(short_name=paths.short_name)
    ds = paths.create_dataset(tables=[tb])
    ds.save()

Step 4 — Garden step

etl/steps/data/garden/<ns>/<date>/<short>.py:

• harmonize country/region names via paths.regions.harmonize_names(tb, country_col="country", countries_file=paths.country_mapping_path) (skip if entities are non-geographic, e.g. food products)
• convert types and units to ETL conventions (long format with country/year index)
• optionally compute per-capita or per-region aggregates
• write *.meta.yml with origins, descriptions, per-indicator metadata

FAUST hint. While authoring indicator metadata, push chart text/config (title_public, subtitle, note, map.colorScale, hasMapTab, tab, yAxis, …) into each indicator's presentation.{title_public, grapher_config}. Cross-cutting baselines go under definitions.common.presentation.grapher_config (recursive merge). The explorer view inherits this text at chart render time, so the <short>.config.yml in the next skill stays minimal. See /create-explorer Step 5 for the full mechanics and the dynamic-yaml "{definitions.<key>}" interpolation pattern.

CSV-explorer migrations have a slight advantage: you control the garden output, so you can shape the new garden table to be dimensional (one row per country/year × dim_a × dim_b) and the export step in the next skill can use paths.create_collection(tb=tb, indicator_names=..., dimensions=...) to expand views directly.

Step 5 — Grapher step

etl/steps/data/grapher/<ns>/<date>/<short>.py — read the garden table and save as a grapher dataset (long format country/year/value).

Step 6 — Optional: pre-fill the explorer YAML from the legacy config

Before invoking /create-explorer, you can seed <short>.config.yml with a partial config extracted from the legacy MySQL explorer:

# In a notebook or one-off script
from etl.collection.explorer.migration import migrate_csv_explorer
config = migrate_csv_explorer("<slug>")  # reads from MySQL
import yaml
print(yaml.safe_dump(config, sort_keys=False))

migrate_csv_explorer only handles CSV-flavored explorers (it raises if the explorer mixes types), and it returns a partial YAML config with dimensions, views, settings, and per-indicator display overrides already mapped from the TSV blocks. The catch: catalog paths in the output point at the explorer table URI in the DB, which usually isn't the grapher dataset path. Rewrite each catalogPath to match the grapher dataset created in step 5 (e.g. <ns>/<date>/<short>/<table>#<short>).

Step 7 — Hand off to /create-explorer

Invoke /create-explorer to author etl/steps/export/explorers/<ns>/latest/<short>.{py,config.yml} and the DAG entry. That skill covers:

• the Python skeleton (full-YAML or table-driven)
• the YAML schema (config:, definitions.common_views, dimensions:, views:)
• block-style YAML rule, hyphens vs underscores
• DAG entry, verification, common pitfalls

If /create-explorer was already invoked once and you only need to refine the layout, you don't need it again — edit the YAML directly.

Reference: existing migrations

There are no fully CSV-backed explorers in ETL today (fish-stocks is the lone remaining target). For the indicator-based pieces of the workflow that the export step will lean on, see the reference examples listed in /create-explorer.

Follow-up

Once in ETL, the explorer is a candidate for the Track-B port to MDIM (export://multidim/...) once feature parity is reached. See umbrella issue #6014.